Daily Archives: February 10, 2020

(Left to right): NASA/GODDARD SPACE FLIGHT CENTER SCIENTIFIC VISUALIZATION STUDIO; ECOHEALTH ALLIANCE; CECIL H. FOX/SCIENCE SOURCE

By Rodrigo Prez Ortega Feb. 7, 2020 , 1:30 PM

Global warming is speeding up Earths massive ocean currents

For nearly 25 years, ocean currents have been rapidly speeding up, partly because of global warming, according to new research.Based on observations and models, study authors claim that from 1990 to 2013, theenergy of the worlds currents increasedby some 15% per decade.

Mining coronavirus genomes for clues to the outbreaks origins

Scientists are publicly sharing an ever-growing number of full sequences of the 2019 novel coronavirus (2019-nCoV)53 at last count in theGlobal Initiative on Sharing All Influenza Datadatabase. These viral genomes are being intensely studied to try to understand the origin of 2019-nCoV and where it fits on the family tree of related viruses found in bats and other species. They have also given glimpses into what this newly discovered virusphysically looks like,how its changing, andhow it might be stopped.

Massive cancer genome study reveals how DNA errors drive tumor growth

The largest ever study to analyze entire tumor genomes has provided the most complete picture yet of how DNA glitches drive tumor cell growth. Researchers say the results,released on Wednesdayin six papers inNatureand 17 in other journals, could pave the way for full genome sequencing of all patients tumors. Such sequences could then be used in efforts to match each patient to a molecular treatment.

Spider biologist denies suspicions of widespread data fraud in his animal personality research

Its been a bad couple of weeks for behavioral ecologist Jonathan Pruitt, and it may get a lot worse. What began withquestions about data in one of Pruitts papers has flared into a social mediafueled scandal in the small field of animal personality research, with dozens of papers on spiders and other invertebrates being scrutinized by scores of students, postdocs, and other co-authors for problematic data.

Colombias first ever science minister faces calls to resign over fungi-based cancer treatment

Biologist Mabel Gisela Torres Torres, the new head of Colombias newly created Ministry of Science, Technology and Innovation, has already been asked by fellow researchers to resign over her controversial claims that a fungal extract can improve the health of cancer patients. But Torres says she wont step down. In response, the Colombian Association of Medical Faculties issued a statement, saying: We can only regret that the course of how to do science in our country has been left in the hands of pseudoscience.

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Top stories: Faster ocean currents, coronavirus DNA, and the largest-ever study of cancer genomes - Science Magazine

Written by Seema Chishti | New Delhi | Updated: February 10, 2020 11:15:39 am The project is said to be among the most significant of its kind in the world because of its scale and the diversity it would bring to genetic studies.

LAST WEEK, The Indian Express reported that the government has cleared an ambitious gene-mapping project that is being described by those involved as the first scratching of the surface of the vast genetic diversity of India. A look at the objectives, scale and the diversity of the project, which will be significant not only in India but worldwide:

What is a genome?

Every organisms genetic code is contained in its Deoxyribose Nucleic Acid (DNA), the building blocks of life. The discovery that DNA is structured as a double helix by James Watson and Francis Crick in 1953, for which they won a Nobel Prize in 1962, was the spark in the long, continuing quest for understanding how genes dictate life, its traits, and what causes diseases.

A genome, simply put, is all the genetic matter in an organism. It is defined as an organisms complete set of DNA, including all of its genes. Each genome contains all of the information needed to build and maintain that organism. In humans, a copy of the entire genome more than 3 billion DNA base pairs is contained in all cells that have a nucleus.

Hasnt the human genome been mapped before?

The Human Genome Project (HGP) was an international programme that led to the decoding of the entire human genome. It has been described as one of the great feats of exploration in history. Rather than an outward exploration of the planet or the cosmos, the HGP was an inward voyage of discovery led by an international team of researchers looking to sequence and map all of the genes together known as the genome of members of our species.

Beginning on October 1, 1990 and completed in April 2003, the HGP gave us the ability, for the first time, to read natures complete genetic blueprint for building a human being.

What then is the Genome India Project?

This is being spearheaded by the Centre for Brain Research at Bengaluru-based Indian Institute of Science as the nodal point of about 20 institutions, each doing its bit in collecting samples, doing the computations, and then the research. Its aim is to ultimately build a grid of the Indian reference genome, to understand fully the type and nature of diseases and traits that comprise the diverse Indian population. For example, if the Northeast sees a tendency towards a specific disease, interventions can be made in the region, assisting public health, which make it easier to battle the illness.

Editorial | Genome India Project is extremely promising and should proceed with maximum speed and maximum caution.

The other institutes involved are: AIIMS Jodhpur; Centre for Cellular and Molecular Biology, Hyderabad; Centre for DNA Fingerprinting and Diagnostics; Institute of Genomics and Integrative Biology; Gujarat Biotechnology Research Centre; IIIT Allahabad; IISER (Pune); IIT Madras; IIT Delhi; IIT Jodhpur; Institute of Bioresources And Sustainable Development; Institute of Life Sciences; Mizoram University; National Centre for Biological Sciences; National Institute of Biomedical Genomics; National Institute of Mental Health and Neurosciences; Rajiv Gandhi Centre for Biotechnology; and Sher-e-Kashmir Institute of Medical Sciences.

So, what will the project broadly do?

The mega project hopes to form a grid after collecting 10,000 samples in the first phase from across India, to arrive at a representative Indian genome. This has been found necessary as over 95% of the genome samples available, which are the basis of new, cutting-edge research in medicine and pharmacology, use the white, Caucasian genome as the base. Most genomes have been sourced from urban middle-class persons and are not really seen as representative. The Indian project will aim to vastly add to the available information on the human species and advance the cause, both because of the scale of the Indian population and the diversity here.

Who is an Indian?

The Indian subcontinent has been the site of huge migrations. Scientists associated with the project recognise that while the first migrations were from Africa, later too there were periodic migrations by various populations, making this a very special case of almost all races and types intermingling genetically. This can be seen as horizontal diversity. Moreover, later, there has been endogamy or inter-marriage practised among distinct groups, resulting in some diseases passed on strictly within some groups and some other traits inherited by just some groups. This is what scientists term vertical diversity.

Studying and understanding both diversities would provide the bedrock of personalised healthcare for a very large group of persons on the planet.

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What are the challenges involved?

MEDICAL ETHICS: In a project that aims only to create a database of genetic information, gene modification is not among the stated objectives. It is important to note, however, that this has been a very fraught subject globally. The lure to intervene may be much more if this kind of knowledge is available, without one being fully aware of the attendant risks. The risk of doctors privately running away with the idea of fixing genetic issues came to light most recently after a Shenzen-based scientist, who helped create the worlds first gene-edited babies, was sentenced to three years in prison. He Jiankui stunned the world when he announced in 2018 that twin girls had been born with modified DNA to make them HIV-resistant. He claimed he had managed that using the gene-editing tool CRISPR-Cas9 before their birth.

DATA & STORAGE: After collection of the sample, anonymity of the data and questions of its possible use and misuse would need to be addressed. Keeping the data on a cloud is fraught with problems and would raise questions of ownership of the data. India is yet to pass a Data Privacy Bill with adequate safeguards. Launching a Genome India Project before the privacy question is settled could give rise to another set of problems.

SOCIAL ISSUES: The question of heredity and racial purity has obsessed civilisations, and more scientific studies of genes and classifying them could reinforce stereotypes and allow for politics and history to acquire a racial twist.

In India a lot of politics is now on the lines of who are indigenous people and who are not. A Genome India Project could add a genetic dimension to the cauldron.

Selective breeding has been controversial since time immemorial, and well before the DNA was discovered. But eugenics acquired a dangerous context with the Nazis deliberating on the theme at length and its mention came up in the Nuremberg trials. Post World War-2, it has been a very touchy issue.

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Explained: Mapping the Indian genome - The Indian Express

Abstract

The first sequenced genome was that of the 3569-nucleotide single-stranded RNA (ssRNA) bacteriophage MS2. Despite the recent accumulation of vast amounts of DNA and RNA sequence data, only 12 representative ssRNA phage genome sequences are available from the NCBI Genome database (June 2019). The difficulty in detecting RNA phages in metagenomic datasets raises questions as to their abundance, taxonomic structure, and ecological importance. In this study, we iteratively applied profile hidden Markov models to detect conserved ssRNA phage proteins in 82 publicly available metatranscriptomic datasets generated from activated sludge and aquatic environments. We identified 15,611 nonredundant ssRNA phage sequences, including 1015 near-complete genomes. This expansion in the number of known sequences enabled us to complete a phylogenetic assessment of both sequences identified in this study and known ssRNA phage genomes. Our expansion of these viruses from two environments suggests that they have been overlooked within microbiome studies.

Viruses, particularly bacteriophages targeting prokaryotes, are the most diverse biological entities in the biosphere (1, 2). Currently, there are 11,489 genome sequences available in the NCBI (National Center for Biotechnology Information) Viral RefSeq database (version 94). The vast majority of known phage have a double-stranded DNA (dsDNA) genome (3, 4). Recent metagenomic analysis of 145 marine virome sampling sites identified 195,728 DNA viral populations, highlighting that only a fraction of Earths viral diversity has been characterized (5). An additional expansion of known phage populations by Roux et al. (6) revealed that not only dsDNA phages but also single-stranded DNA Inoviridae are far more diverse than previously considered. The rapid expansion in viral discovery through metagenomics is enabling a greater understanding of their roles within environments and their evolutionary relationships, which is subsequently causing a revolution in phage taxonomy (7).

Despite the identification of single-stranded RNA (ssRNA) phages over 50 years ago (8), there are few representative sequences available. The International Committee on Taxonomy of Viruses (ICTV) has currently categorized approximately 5500 viruses (9). Yet, their classification only applies to 25 ssRNA phage sequences (complete or partial) across two genera, Levivirus and Allolevivirus, and an additional 32 sequences unclassified below a family taxonomic rank (10). Historically, methods for classifying Leviviridae depended on molecular weight, density, sedimentation, and serological cross-reactivity (11). A subsequent classification method separated the two genera, with the Alloleviviruses containing a fourth unique gene predicted to encode a lysin (12). Recently, an analysis of the evolution origin of all currently known RNA viruses by Wolf et al. (13) suggested that ssRNA phages may actually be two distinct lineages, which they termed Leviviridae and Levi-like viruses.

The ssRNA phage MS2 is a non-enveloped virus with a positive-sense monopartite genome of 3569 nt and was the first biological entity to have its entire genome sequenced (14). MS2 and its relatives were assigned to the family Leviviridae and were generally isolated against Proteobacteria. With additional studies, we can anticipate that ssRNA phages will be found, which target additional bacterial phyla. Genomes of ssRNA phages encode a maturation protein (MP) responsible for host recognition, a coat protein (CP) for genome encapsulation, and an RNA-dependent RNA polymerase (RdRp) required for viral replication. During the phage replication process, there is a negative-sense template produced for genome replication, although it does not persist and no negative-sense ssRNA phages have been isolated or characterized to date (15).

An analysis of the evolution of all RNA viruses recently proposed their primordial origin from reverse transcriptases. ICTV has recently established a new viral realm, Ribovira, to incorporate all known RNA viruses, as they all encode an RdRp for replication (16). The origin of ssRNA phages followed the acquisition of a CP, potentially allowing them to survive ex vivo and prey on the first cellular microbes (13). Despite their small genome size (encoding only three or four genes), ssRNA phages have served as models for understanding some of natures most widespread fundamental processes, including genome secondary structure to mechanisms of controlling gene expression and genome replication (17, 18).

Identification of phages was traditionally dependent on culture-based methods (19). In recent years, there has been a shift to culture-independent metagenomic approaches that aim to capture all microbial genomes within a given environment (20). An analysis by Krishnamurthy et al. (21) identified 158 ssRNA phage sequences (complete and partial), remarkably expanding the previously recognized diversity of this group. A more recent study by Starr et al. (22) demonstrated that metatranscriptomics will advance ssRNA phage discovery, with 1338 ssRNA phage RdRp sequences detected in soil. Metatranscriptomics is indeed well suited to capturing ssRNA phage sequences in complex biological samples, given that their genomes resemble the mRNA transcripts that are targeted by this method.

The actual abundance and diversity of ssRNA phages have remained unknown despite recent advancements to better study the phage populations of different environments. Databases are dominated by DNA phage genomes, and novel ssRNA phages may not be recognized. Isolation and purification techniques for phages, such as caesium chloride (CsCl) gradient purification and polyethylene glycol, are biased toward isolating specific phage types (23). Even accepting that specific metatranscriptomic approaches will introduce their own biases in the process of removing ribosomal RNA (24), it is likely to be more representative of the RNA composition of a specific microbiome, including the RNA viral contingents.

RNA phages have served as key models in understanding some of biologys most intricate pathways such as gene regulation. These phages also offer a potential option in terms of phage therapy, as they have been isolated against many pathogenic bacteria including Acinetobacter and Pseudomonas. Fundamentally, the expansion in ssRNA phage genomes reported here demonstrates that their contributions to the diversity of ecological niches and their impacts on their associated hosts may have been underestimated. Given that we are just starting to explore Earths viral dark matter through metagenomics, it seems fitting that a portion of this unexplored viral diversity is represented by phages that are not encoded by DNA.

In this study, we report the identification of 15,611 near-complete and partial ssRNA phage sequences. Of these, 1015 were defined as near complete in that they encode all three MP, CP, and RdRp genes that form the recognized ssRNA phage core genome. The identification of ssRNA phage sequences was performed by iteratively developing and applying hidden Markov models (HMMs) based on conserved ssRNA phage proteins. We applied these HMMs to ever-increasing samples from 70 activated sludge and 12 aquatic environments. This expansion in the number of ssRNA phage genomes enabled us to examine the phylogenetic relationships between sequences identified in this study and known sequences and perform a preliminary investigation of phage-host interactions.

We collected 193 identifiable unique partial ssRNA phage genome sequences from publicly available databases and relevant studies (fig. S1). An additional 67 Levi-like sequences, described by Shi et al. (25), were used to validate the identification of ssRNA phages from an RNA viral database (see Materials and Methods). We predicted the encoded proteins of the 193 ssRNA phage genomes and used a graph-based clustering method to build a database of HMM sequence profiles representative of their protein sequences (see fig. S2 and Supplementary Text). Four subsequent HMM iterations were built, each using the previous HMM output, and were applied to a final total of 82 publicly available environmental metatranscriptome samples generated from globally sourced activated sludge and aquatic samples. A final manually curated HMM, designated 5-MC, was developed by removing all partial protein sequences.

In total, we identified 15,611 ssRNA phage genomes or partial sequences (Fig. 1B). This represents an approximately 60-fold increase in the number of partial genome sequences. Of the 15,611 identified sequences, there were 5387 ssRNA phage sequences, which had a minimum length of 750 base pairs (bp) and included at least one core gene (MP, CP, or RdRp), 2987 included two core genes, and 1848 had sequences from all three core genes. Of these, 1015 are predicted to encode full-length core genes (see Supplementary Text). Only 29 of the currently publicly identifiable 193 ssRNA phage sequences meet this same criterion (fig. S1D).

(A) The total number of redundant contigs detected per HMM search. (B) The manually curated HMM 5-MC detected 15,611 nonredundant ssRNA phage sequences. Boxplot displays the median value within the 25th and 75th quartiles, with whiskers representing the interquartile range of 1.5. (C) The number of contigs (near complete or partial) detected per assembly in activated sludge and aquatic samples. Boxplot horizontal lines indicate the mean, while the gray boxes represent 95% highest-density intervals. (D) Two-dimensional ordination of ssRNA compositional abundance across different geographical locations using the Bray-Curtis Dissimilarity index. The colors and shapes of individual samples differentiate study location and environment, respectively. PC, principle component. (E) Linear model of metatranscriptome sequencing coverage and contig length. Contigs included are of minimum length of 750 bp, and the number of core proteins encoded is indicated.

Significantly more ssRNA phage sequences were detected in activated sludge than in aquatic samples (Kruskal-Wallis, P = 1.847 106; Fig. 1C). It is possible that activated sludge provides an environment in which proteobacteria, the only known hosts for ssRNA phages, can grow and support phage enrichment. The higher levels of detection could also be due to a variety of technical factors such as increased sequencing depth, microbiome complexity, and metatranscriptome sampling protocols. Our ability to detect longer ssRNA phage sequences correlates with metatranscriptome sequencing depth (Fig. 1E).

The 15,611 ssRNA phage sequences encoded 24,419 proteins that could be grouped into three MP, eight CP, and two RdRp clusters (Fig. 2A and fig. S2). It is evident that the RdRp is the most conserved protein, forming only two clusters, whereas the CP is the most diverse of the ssRNA phageassociated core protein, splitting into eight clusters. We next examined all 2987 ssRNA sequences encoding at least two core proteins, which revealed two highly distinct groups (Fig. 2B). Only 5 of the almost 3000 assembled sequences bridge the two groups, and these were investigated further (see Supplementary Text). Briefly, the five outliers only encode partial rather than complete proteins, and their relatedness to a specific protein cluster may be driven by local rather than global sequence similarity.

(A) Distribution of protein hits (in parentheses) across MP, CP, and RdRp clusters was identified using HMM 5-MC. (B) Bipartite connection network of contigs (circles) with proteins (squares). Colors are based on the associated CP from (A). (C) Protein cluster co-occurring profiles of ssRNA phages having all three full-length core proteins and (D) the frequently observed positions of hypothetical proteins (genes not drawn to scale).

We analyzed all 1015 near-complete ssRNA phage genomes and observed strictly conserved protein associations (Fig. 2C). In contrast to other viruses, there are no obvious instances of homologous recombination and mosaicism among the identified ssRNA phages. Both mosaicism and horizontal gene transfer are well noted for dsDNA phages, with single genes and whole modules exchanged (26, 27). Recombination frequencies of RNA viruses are reported to vary markedly during coinfection, influenced by various factors such as sequence identity, kinetics of transcription, and RNA genome secondary structure (28). We only recorded eight protein connection profiles between the three MP, eight CP, and two RdRp protein clusters of ssRNA phages. If their genomes underwent extensive recombination events, then it would be expected that the number of core-protein connection profiles would be closer to the theoretical maximum of 48 (3 MP 8 CP 2 RdRp). However, as our ssRNA phage discovery pipeline is restricted to finding viruses encoding core proteins similar to those previously identified, future studies with less stringent search criteria may uncover additional unexplored biodiversity.

With such a tremendous expansion in the quantity of identifiable complete ssRNA phages, we undertook an examination of their genome structure. First, we investigated the specific order of MP, CP, and RdRp core proteins. Notably, on no occasion did we identify the recognizable CP situated either before the MP- or after the RdRp-encoding genes. In all 1015 instances, a CP was situated between the MP and RdRp genes. We noted that hypothetical proteins could exist before the MP, after the CP, or following the RdRp (Fig. 2D). In 20 instances, there were two hypothetical proteins situated before the MP. We termed the locations the alpha position (closest to the 5 terminus), the beta position (between the CP and RdRp), and the gamma position (closest to the 3 terminus). We labeled any hypothetical immediately preceding the MP gene as the alpha 1 position, and if a second hypothetical was identified, then it was deemed to occupy the alpha 2 position.

Further investigation revealed that hypothetical genes predicted to follow the RdRp often had weak similarity to the native RdRp termini. Therefore, these proteins annotated as hypothetical could be an artifact of stop codons inadvertently introduced during metatranscriptome assembly, or alternatively, RNA phages are known to bypass stop codons as part of their replication (29). The hypothetical genes located upstream of the MP and between the CP and RdRp were also analyzed. These genes encode proteins with high sequence diversity, and hence, they did not generate clusters. However, several isolated ssRNA phages are known to contain a gene encoding a lysin in these positions (12, 30). These hypothetical genes may encode this and/or other putative functions, which may be revealed in future studies through biochemical analysis.

Comparisons of RNA viruses infecting all kingdoms of life have previously been undertaken using the RdRp protein (13). For greater resolution, we estimated the evolutionary relatedness of ssRNA phages using all three core proteins. We included the 29 publicly identifiable complete ssRNA phage sequences with the 1015 identified in this study. Through phylogenetic analysis, we observed the higher-level taxonomy of ssRNA phages that follow the clustering of the RdRp and CP (Fig. 3A). Lower-level taxonomy of ssRNA phages was performed using pairwise identity comparisons (fig. S5). A potential restructuring of ssRNA taxonomy is outlined in (fig. S6).

Phylogeny of ssRNA phages using their core protein sequences (MP, CP, and RdRp). The 29 previously characterized and 1015 newly identified phages were included. Branch tip shapes highlight specific RdRp protein clusters, while color indicates CP clustering. The encircling annotation ring depicts current ICTV taxonomy. A green arrowhead represents AVE006, which encodes a unique RdRp and CP association. Bootstrap support values shown are for 100 iterations.

The phylogenetic divergence of ssRNA phages by their core proteins supports the hypothesis of Wolf et al. (13) that the current Leviviridae family is two distinct lineages. However, our analysis further classifies ssRNA phages into eight subfamilies (currently denoted A to H) based on CP clustering. While this suggested that classification system can be applied to previously identified ssRNA phages, it does not support the current Levivirus and Allolevivirus taxonomic division (Fig. 3 and fig. S6).

Correlation analysis between the newly proposed taxa and the source locations identified a possible link. The ssRNA subfamilies were statistically different by geographical location (Kruskal-Wallis test; P < 0.001). This may signify that specific ecological niches are occupied by specific phage taxa. For example, CP A was strongly associated with ssRNA phages identified from the Illinois study site (254 of 1015; 25.0%), whereas it was infrequently observed among Singapore-associated phages (0.5%). A specific global distribution of dsDNA phages was recently detailed for crAssphage (31). However, because of the inherent differences introduced through different study protocols and sequencing methodologies, a single study investigating multiple geographical locations is necessary to confirm the potential global localization of specific ssRNA phage taxa.

In an attempt to further elucidate ssRNA phage interactions with their host bacteria, we examined bacterially encoded CRISPR systems and also examined the phage receptor binding protein, MP. CRISPR systems have recently been identified to target RNA phages (32); however, actively transcribed CRISPR spacers were only found against a handful of viral RefSeq database sequences in this study (see fig. S7B and Supplementary Text). No CRISPR spacers were identified to target ssRNA phage sequences. A similar observation was noted by Silas and colleagues (33). Therefore, advances in alternative techniques may be required to identify ssRNA phagehost partners, as has been demonstrated for dsDNA phages using Hi-C sequencing and single-cell viral tagging (34, 35).

Our expanded number of host-recognizing MP protein sequences allowed for a comparative structural analysis. Focusing on the MP cluster A, we revealed three variable regions associated with the MP -binding region (fig. S8). Conserved and variable regions of MP proteins were previously highlighted during an analysis of ssRNA phage AP205 (36). Structural analysis of cluster A host-recognizing MP proteins also revealed the association of different sections with various viral components, with the conserved -helical domain interacting with the CP subunits and the viral genome. The identification of variable ssRNA phage genomic regions through multiple sequence comparisons will further reveal areas under evolutionary selective pressure.

In summary, we iteratively optimized an HMM-based ssRNA phage discovery pipeline. Through intensive data mining of multiple metatranscriptomic datasets from just two environmental ecosystems, we identified 15,611 near-complete and partial genomes. These samples originated from America, Austria, Japan, and Singapore, highlighting the global distribution of these viruses. This represents an approximate 60-fold expansion of previously known genome sequences. Phylogenetic comparison of 1044 near-complete genomes allowed us to construct a robust, yet elastic, taxonomic scheme that provides a hierarchal foundation, which will accommodate the expected increase in ssRNA phage discoveries. Given the amount of the ssRNA phages identified in this study from two environments, we suspect that their low abundance in metagenomic studies of other ecosystems may be attributed to a variety of factors, including isolation protocols and computational shortcomings.

The assembly of metatranscriptome samples is portrayed in fig. S2A. Fastq raw reads were downloaded from the NCBI Sequence Read Archive (SRA) database using accession numbers provided in the Supplementary Materials, with files separated into forward and reverse reads using the --split-files option. Illumina adapter sequences were removed using Cutadapt [version 1.9.1; (37)]. The overall read quality was improved using Trimmomatic [version 0.32; (38)], pruning sequences where the read quality dropped below a Phred score of 30 for a 4-bp sliding window. Reads less than 70 bp were discarded, with surviving reads assembled using rnaSPAdes [version 3.12.0; (39)]. Only metatranscriptome sample SRR5466337, which generated an error during rnaSPAdes assembly, was assembled differently using MEGAHIT [version 1.1.1-2; (40)]. This one sample, of the total 82 samples, failed to assemble using rnaSPAdes. The reasons were not investigated further. All contig assemblies less than 500 bp were discarded. Only the rnaSPAdes hard filtered transcript outputs were examined for the presence of ssRNA phages.

The pipeline for generating profile HMMs is depicted in fig. S2B, with the numerical breakdown of the HMM building and testing stages depicted in fig. S2 (D and E), respectively. To generate the first HMM, HMM 1, all ssRNA phage near-complete and partial genome sequences were downloaded from the NCBI Taxonomy database (October 2018) and previous published studies (21). The encoded proteins of all identifiable ssRNA phage sequences (n = 193) were predicted using Prodigal with the -p meta option enabled for small contigs, and -n option was specified to do a full motif scan per nucleotide sequence [version 2.6.3; (41)]. Predicted proteins were clustered using OrthoMCL using a BLASTp all-v-all E value of 1 105 and default settings [version 2.0; (42)]. Clusters of ssRNA phage proteins with 10 or more sequences were aligned using MUSCLE [version 3.8.31; (43)] and used to generate HMMs via hmmbuild [version 3.1b1; (44)]. Multiple HMMs were combined into a single HMM search tool through hmmpress (version 3.1b1).

The number of samples tested by each HMM iteration is outlined in fig. S2C. HMMs 2 to 5 were built in a similar fashion to HMM 1 with the following alterations. Subsequent to the detection of contigs in metatranscriptome samples encoding two or more functionally distinct ssRNA phage proteins (hmmscan score of 50 or greater), the predicted proteins were combined with those from the initial 193 ssRNA phage sequences, obtained from NCBI and a previous publication (21). Using a BLAST all-v-all approach, the proteins used to generate HMMs 2 to 5 were made nonredundant at 70% amino acid identity, removing the shorter of two protein sequences when the overlap exceeded 70%. Before the generation of HMM 5-MC, proteins were manually curated to remove sequences encoded at the edge of contigs (termed edge proteins).

The metatranscriptome sample SRR1027978, which was an activated sludge sample previously shown by Krishnamurthy et al. (21) as containing ssRNA phage sequences using a tBLASTn approach, was downloaded as a positive control and examined for the presence of ssRNA phage proteins. Briefly, a random subset of 10 million reads was extracted from the SRA file with the seqtk sample command [version 1.0-r31; (45)] using a user-defined seed (-s13). Adaptor and read trimming was performed as described above, with surviving reads assembled using MEGAHIT. Proteins were predicted in all contigs greater than 500 bp, using options -p meta -n, before scanning with HMM 1.

After manual curation of ssRNA phage hits, it was decided to adopt a conservative approach for the remainder of the study. Only hmmscan hits with a score of 50 or greater were considered during the generation of HMM iterations, with hmmscan scores of 30 further investigated during metatranscriptome sample analyses. Future studies may benefit from less stringent ssRNA phage discovery cutoffs, by lowering the hmmscan score requirements and/or using rnaSPAdes soft filtered transcripts. However, results would need to be treated cautiously to avoid false positives.

A comparison between a BLAST and an HMM-based approach to identify ssRNA phages was performed using the complete ssRNA phage proteins, which built the final HMM model 5-MC. The BLAST and HMM approaches were applied to the 2308 unique viral sequences described by Shi and colleagues (25). This database contains 67 ssRNA Levi-like viruses. Using a relaxed BLASTp E value of 1 105, 78 viral sequences were considered ssRNA phages (11 false positives). However, with a more stringent BLASTp E value of 1 1015, only the expected 67 sequences were returned. Using an HMM scan with a score of 30 identified the 67 Levi-like viruses without any false positives identified.

When the strict BLASTp search approach (E value of 1 1015) was applied to the assembled contigs from the metatranscriptome sample SRR1027978, 12 ssRNA phages were identified. The HMM-based approach identified 13 ssRNA phages. Reducing the BLASTp stringency to 1 105 did identify 13 putative ssRNA phages. However, because of the false positives noted while using a less strict BLASTp approach against a curated database, only HMM searches were used throughout this study.

After confirming that HMM 1 could detect ssRNA phage proteins in a positive control sample, HMM 1 was implemented against nine previously untested metatranscriptome samples of activated sludge. This environment was chosen as Krishnamurthy et al. (21) demonstrated sewage as a rich source for ssRNA phages (21). These nine SRA files analyzed represent three activated sludge samples from each of the study locations from Austria, Illinois, and Japan (see the Supplementary Materials). The total collection of activated sludge and aquatic samples cumulatively analyzed during this study is outlined in fig. S2C. The remaining samples tested represent 13 activated sludge samples from Austria, 39 activated sludge samples from Illinois, 9 activated sludge samples from Japan, 4 freshwater aquatic samples from Lake Mendota (Wisconsin), 4 aquatic samples from the Mississippi river (Louisiana), and 4 freshwater aquatic samples from Singapore.

Analyses were conducted using the R programming language (version 3.5.3) implemented through RStudio (46). Images were generated using the ggplot2 package (47), with additional colors obtained from the RColorBrewer (48), the wesanderson (49), and the YaRrr package (50). The bipartite network of ssRNA phage proteins, for sequences containing two or three core proteins, was generated using the igraph package (51). The distance between core proteins (squares) was automatically calculated on the basis of the number of ssRNA sequences (circles) that share similar protein profiles. The ssRNA phage partial genomes are colored on the basis of the associated CP. The Sankey plot demonstrating the connection patterns of ssRNA phageencoded proteins was illustrated using the R package networkD3 (52).

Phylogeny of ssRNA phage proteins was performed as follows. Proteins fulfilling the same functions among ssRNA phages were assigned the name of their originating contig and subsequently aligned using MUSCLE. The alignment of the three core proteins were concatenated using MEGA [version 10.0.5; (53)]. After the three proteins were concatenated, the MUSCLE alignment was performed with default settingsno alignment trimming, all positions were retained, and the substitution model was applied to all proteins together. These alignments were imported into R using the seqinr package (54, 55) with ape package dependencies (56) before conversion to a phyDat format using the phangorn package (57). The best evolutionary model was estimated using the phangorn modelTest function, with the model yielding the lowest Akaike Information Criterion score selected for maximum likelihood tree construction. Blosum62 was determined as the best amino acid substitution model. Phylogenetic trees were bootstrapped 100 times and saved using the treeio package (58), before visualization using ggtree (59). The R scripts and input data used to generate this studys images and infer results are provided in the Supplementary Materials.

The newly identified unique RNA phage sequences and genomes (n = 15,611 and 1015, respectively) and the final ssRNA phage detection tool (HMM 5-MC) are provided in data S1. All the accession number details, raw data, tables, and R scripts used in the analysis and creation of images are provided in data S2.

Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/6/6/eaay5981/DC1

Supplementary Text

Fig. S1. Workflow depiction of known ssRNA phage sequences.

Fig. S2. Workflow depiction of the study pipeline.

Fig. S3. Identification of ssRNA phage contigs within 82 metatranscriptome samples.

Fig. S4. Genome architecture of ssRNA phages.

Fig. S5. Taxonomic cutoff values for ssRNA phage genera and species.

Fig. S6. Potential taxonomic restructuring for ssRNA phages.

Fig. S7. Analysis of microbial community complexity.

Fig. S8. Structural investigation of ssRNA phagehost interactions.

Data S1. ssRNA phage finding hidden Markov model and associated sequences.

Data S2. Bioinformatic scripts used during data analysis.

References (6078)

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

Acknowledgments: Funding: This publication has emanated from research conducted with the financial support of Science Foundation Ireland under grant number SFI/12/RC/2273. Author contributions: J.C. and S.R.S. conceived the study, performed the analysis, produced the images, and wrote the manuscript. A.S. and L.A.D. helped interpret the results, provided helpful suggestions, and corrected manuscript drafts. R.P.R. and C.H. secured the funding, conceived the study, contributed to data analysis, and assisted in generating the final manuscript. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

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Expansion of known ssRNA phage genomes: From tens to over a thousand - Science Advances

Four thousand years ago, the last woolly mammoths quietly died on their final bastion - the isolated Wrangel Island, north of Russia in the frozen Arctic. Their demise was sudden, and strange; now, new evidence points to the mammoths themselves as partial agents of their own demise.

Specifically, the mammoths(Mammuthus primigenius) were afflicted by genetic diseases, likely brought about by a lack of genetic diversity. As their numbers declined and the pool of available mates grew ever smaller, detrimental genetic mutations increased, resulting in an increasingly unhealthy herd.

The evidence is compelling, as it's not just genome sequencing. Scientists actually raised the mammoths' genes from the dead, and placed them in elephant embryo cells in the lab to see how well they functioned.

The answer is: Not well at all. The genes were sad, stumbling, broken things that could have seriously impaired important functions, such as male fertility, and the mammoths' sense of smell.

"The key innovation of our paper is that we actually resurrect Wrangel Island mammoth genes to test whether their mutations actually were damaging (most mutations don't actually do anything)," said evolutionary biologist Vincent Lynch of the University at Buffalo.

"Beyond suggesting that the last mammoths were probably an unhealthy population, it's a cautionary tale for living species threatened with extinction: If their populations stay small, they too may accumulate deleterious mutations that can contribute to their extinction."

The death of the mammothon Wrangel Island has been the subject of a number of studies. Last year, isotope analysis of the bones and teeth of the animals - which can reveal what the deceased ate over the course of their life - pieced together dramatic changes in the mammoths' diet that point to dramatic environmental changes.

Prior to that research, scientists had conducted complete genome sequencing on Wrangel Island woolly mammoths along with earlier, more healthy mammoth populations. The results were published in 2017; in those genomes, the scientists found "accumulation of detrimental mutations ... consistent with genomic meltdown."

The new research builds on that 2017 paper."The results are very complementary," Lynch said.

"The 2017 study predicts that Wrangel Island mammoths were accumulating damaging mutations. We found something similar and tested those predictions by resurrecting mutated genes in the lab."

Lynch and his colleagues identified detrimental mutations by comparing the genome of the Wrangel Island mammoths to their living relatives - three Asian elephants (Elephas maximus).

They also compared it to the genomes of two other mammoths - one from 44,800 years ago, and the other from 20,000 years ago, when the populations were large and hale.

From these comparisons, they were able to identify mutations related to defects in sperm morphology; neurological development; insulin signaling; and olfactory receptors.

Then, they raised these mutated genes from the dead. They synthesised and cloned the genes, then placed them in gene-edited elephant embryos in a petri dish, so the researchers could observe how proteins expressed by the genes interacted with other genes and molecules.

"We know how the genes responsible for our ability to detect scents work," Lynch explained.

"So we can resurrect the mammoth version, make cells in culture produce the mammoth gene, and then test whether the protein functions normally in cells. If it doesn't - and it didn't - we can infer that it probably means that Wrangel Island mammoths were unable to smell the flowers that they ate."

The animals also likely had higher rates of male infertility, and diabetes, as well as neurological defects. But it's also important to note that this would not have been the only factor contributing to the end of the woolly mammoth.

Their demise began 11,700 years ago, towards the tail end of the last ice age. As the world warmed, and humans (and their hunting) spread, mammoths petered out; by just under 10,000 years ago, the species was extinct from its extensive mainland habitat across Eurasia and North America.

This drastic decline of numbers decreased genetic diversity, which meant that the animals were inbreeding to a higher degree, and less capable of purging bad mutations. We've seen this phenomenon multiple times just prior to a species' extinction, and understanding it is an important tool for conservation.

It's way too late, of course, for the mammoth. But the multiple factors leading up to its lonely end could teach us to do better by the animals still living on our planet.

The research has been published in Genome Biology and Evolution.

Excerpt from:
Ancient Genes Reveal The Last Mammoths on Earth Were a Sickly Mess - ScienceAlert

SINGAPORE (The Straits Times/Ann): Even as masks are flying off the shelves and public spaces thoroughly scrubbed amidst a heightened sense of caution, scientists are hard at work to understand the genome of the novel coronavirus which originated in Wuhan, China.

The genetic material encodes information that is critical in helping scientists develop a diagnostic test and a vaccine for the coronavirus, which has killed more than 400 people and infected tens of thousands since the world first heard of it about a month ago.

The genome of the coronavirus was first uploaded on a public database in early January by scientists in China.

Since then, researchers around the world have been trying to sequence the genome of the virus from samples taken from patients.

Doctors and scientists from Duke-NUS, Singapore General Hospital, National Centre for Infectious Diseases and the Ministry of Health have successfully cultured the coronavirus from an infected patient's clinical sample, only the third country in the world outside of China to do so.

The Straits Times speaks with cell biologist Ong Siew Hwa, director and chief scientist at Acumen Research Laboratories, to understand why the study of the genome of the virus is so important.

Without a host, viruses are just genetic material encased in a protein shell - they cannot reproduce without infecting a mammalian host cell, and they essentially, just exist.

In fact, if a virus is outside a host body, many scientists would not consider them living things.

But when viruses latch on to a host cell - in the case of the novel coronavirus, their mammalian hosts are human - they shed their innocuous nature.

The virus takes over the cellular machinery of the host cell and hijacks it to produce more viral genetic material, on top of the work the machinery does in producing molecules to keep the host healthy and alive.

How do the viruses hijack cells? It all boils down to their genetic make-up.

Viruses of the family Coronaviridae, which includes the novel coronavirus, possess a single-strand RNA genome.

Generally, RNA contains instructions that "tells" a cell what proteins to produce.

So if a host cell is infected by a virus, it goes rogue - the cell takes its cue now from the viral RNA, and produces additional types of proteins than what it is used to.

For patients infected with the novel coronavirus, these additional proteins could lead to an inflammation of the lung tissue and cause pneumonia-like symptoms.

Said Professor Lisa Ng, a senior principal investigator at the Singapore Immunology Network at the Agency for Science, Technology and Research (A*STAR):

"Infection with viruses sometimes cause exuberant immune responses known as 'cytokine storms'. This means excessive levels of pro-inflammatory cytokines will be produced," said Prof Ng.

She added that the production of various pro-inflammatory cytokines will lead to inflammation of the lung tissues.

"Prolonged inflammation could lead to widespread tissue damage. In this case, some of the lung tissues of coronavirus-infected patients may be damaged," said Prof Ng.

The genome of a virus is more than just a package of instructions for a host cell.

Its genome is also unique to it, and can function as a "fingerprint" that help scientists distinguish it from other viruses.

The genome has yielded multiple insights, said cell biologist Ong Siew Hwa, director and chief scientist at Acumen Research Laboratories - a home-grown biotech company.

First, just as how decoding the human genome enabled scientists to establish the evolutionary relationship between humans and chimpanzees, the virus' genome has allowed scientists to confirm that the novel coronavirus is closely related to the virus that caused the severe acute respiratory syndrome (Sars) in 2003.

This could mean that scientists can build on previous research undertaken for a Sars vaccine, instead of starting from scratch, said Dr Ong.

Dr Ong explained: "This close relationship could mean that a vaccine or anti-viral treatment for Sars, if available, could be used to treat patients infected with the novel coronavirus."

Already, China has started a clinical trial to test Remdesivir, a new antiviral drug by Gilead Sciences aimed at infectious diseases such Ebola and Sars.

Doctors at the Beijing-based China-Japan Friendship Hospital are testing the drug for efficacy in treating the deadly new strain of coronavirus, Bloomberg reported.

Second, the availability of the genome of the virus means that diagnostic kits can be developed, said Dr Ong, whose company Acumen Research Laboratories has developed such a kit for the novel coronavirus.

"Such a test can help clinicians quickly separate patients infected by the novel coronavirus from other patients with pneumonia-like symptoms, allowing both groups to get the treatment they need," she told The Straits Times.

Genetic work is a crucial tool in the world's fight to control the outbreak of the novel coronavirus, but it should not be the only weapon in the armoury, say experts.

Clinical studies of patients already infected with the virus are also important, said Dr Ong.

For one, this will allow scientists to determine if the composition of the blood (which includes virus-fighting antibodies) of patients changes over time.

Such studies could also allow clinicians to identify if there are any similar genetic traits among the patients who develop more severe infections, known as sepsis, said Dr Ong.

"If doctors can identify patients who are more at risk of developing severe complications based on certain genetic traits, or DNA biomarkers, it could help them determine which patients to send to intensive care units (ICUs)," said Dr Ong.

"ICUs are a precious resource in hospitals, so being able to identify patients who are more at risk of developing severe pneumonia at an earlier stage could help them manage resources and save lives," she added. - The Straits Times/Asia News Network

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Sequencing genome of the coronavirus from Wuhan could lead to vaccines - The Star Online

SINGAPORE - Time is of the essence when it comes to curbing the spread of the coronavirus (2019-nCoV) that has infected more than 30,000 people worldwide in the span of a month.

Identifying those who may be infected with the virus is especially critical.

Yet, since those infected exhibit pneumonia-like symptoms such as fever and cough - symptoms that could have been caused by other viruses or bacteria - it may not be possible to identify all of them by sight or clinical examinations and routine laboratory tests.

A diagnostic test that can quickly and accurately detect the presence of the virus in a patient is crucial, so that he or she can be quarantined and treated before the disease spreads.

Yet, because so the virus is so new, there has not been a widely implemented test for its detection.

Nations and companies, including those in Singapore, are now working to develop such a diagnostic test kit.

One of them is home-grown biotech company Acumen Research Laboratories.

Its director and chief scientist, DrOng Siew Hwa, said current testing procedures require initial screening and verification steps, in a process that could take up to a day for every one patient sample.

"The latest policy protocol is that all patients exhibiting pneumonia-like symptoms will be tested for the novel coronavirus. This could cause a bottleneck in diagnosis of the patients using the current method," said Dr Ong.

The prototype of a diagnostic test developed by her company has two advantages over the current method, she said.

First, it can analyse at least 24 patient samples at a time. Two, it reduces the time required for sample analysis from about a dayto just about two hours.

Said Dr Ong: "If this prototype is scaled up and produced on a mass scale, it will allow doctors to quickly sieve through all suspected cases, as well as those exhibiting symptoms of pneumonia, from patients who have the virus."

In the conventional testing method, samples such as of lung fluids are first taken from the patient and sent to the clinical testing lab.

Next, lab technologists process the sample using a method known in molecular biology as the polymerase chain reaction (PCR) to determine if the sample contains any detectable genetic material of the coronavirus. This could take up to four hours.

If the results test positive for the coronavirus, the sample will be sent for genome sequencing, to confirm the findings. The initial screening and confirmation by genome sequencing can take up to 24 hours, said Dr Ong.

But the prototype can shorten this process to just about two hours, she said.

Key to its development was the availability of the genome of the virus, which is unique to it, and can serve as a "fingerprint" that helps scientists distinguish it from other viruses.

The genome of the 2019-nCoV was made publicly available last month by scientists in China, where the first patients were identified. This allowed scientists to determine that the new virus was closely related to the virus that caused the severe acute respiratory syndrome (Sars) in 2003.

Dr Ong told The Straits Times: "But even though the genome of 2019-nCoV is about 80 per cent similar to the Sars virus, there were many parts in the genome where they differed."

These differences could function as "red flags" for doctors, if a diagnostic test could highlight them.

In essence, that is what such diagnostic test kits, including the one from Acumen Research Laboratories, can do. Such test kits often differ in the part of the genome they highlight as "red flags".

Similar to conventional methods, doctors collect samples from a patient's lungs or cough mucus (such samples are known as sputum), and process the sample via PCR.

Through the use of the kit and by programming the PCR machine to recognise these "red flags", subsequent analyses would be able to determine if they could be detected.

This reduces the need for additional genetic sequencing, although this could be done if additional verification was required, said Dr Ong. She added that the next step was to try out the test using actual samples of the pathogen collected from patients.

Separately, Dr Ong's firm last September received a $600,000 grant from the Chinese government to develop a kit that would allow doctors to identify patients who have infections, such as pneumonia, and are at a greater risk of developing sepsis.

Sepsis refers to a life-threatening condition caused by the body's response to an infection.

The body normally releases antibodies and chemicals into the bloodstream to fight an infection, but sepsis occurs when the infection-fighting chemicals turn against the host, damaging multiple organ systems.

These sepsis kits are now ready, and can be deployed to China when hospitals there call for it, said Dr Ong. She added that the kits will be sent to hospitals there at cost, as part of the firm's national service to fight the outbreak of the disease.

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Coronavirus: Genomic 'red flags' to determine if patients are infected with virus originating from Wuhan - The Straits Times

US scientists have succeeded in genetically editing the immune systems of three cancer patients using CRISPR, without creating any side effects, a first for the tool which is revolutionizing biomedical research.

The highly anticipated results from the first phase of a clinical trial were published in the journal Science on Thursday.

They represent a stepping stone that doesn't yet prove CRISPR can be used to fight cancer. Indeed, one of the patients has since died and the disease has worsened in the other two - but the trial does show that the technique is non-toxic.

Researchers at the University of Pennsylvania (UPenn) removed T-cells from the patients' blood and used CRISPR to delete genes from the cells that might interfere with the immune system's ability to fight cancer.

They then used a virus to arm the T-cells to attack a protein typically found on cancer cells called NY-ESO-1, and infused the cells back into the patients.

Edward Stadtmauer, the study's principal investigator, told AFP that T-cell therapy, in which a person's own immune system is exploited to destroy tumors, had been a major breakthrough of the past decade, but "unfortunately, even with that technology there are so many patients who don't respond."

The idea of this work therefore is to combine the two cutting-edge approaches to make T-cells even more powerful.

There may not have been major clinical results this time around, but "to me the import of this study is not the clinical results but the fact that we were able to feasibly do this very complex procedure," added Stadtmauer.

Agence France-Presse

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US Trial Shows 3 Cancer Patients Had Their Genomes Altered Safely by CRISPR - ScienceAlert

Written by Mehr Gill, Edited by Explained Desk | Updated: February 8, 2020 8:07:59 pm The project is said to be among the most significant of its kind in the world because of its scale and the diversity it would bring to genetic studies.

On Friday, The Indian Express reported that the government has given clearance to an ambitious gene-mapping project, estimated to be worth Rs 238 crore. The Genome India Project, which has been described by those involved as the first scratching of the surface of the vast genetic diversity of India, involve over 20 scientists from institutions including the Indian Institute of Science (IISc) in Bengaluru and a few IITs.

One of the most comprehensive genome mapping projects in the world is the Human Genome Project (HGP), which began in 1990 and reached completion in 2003. The international project, which was coordinated by the National Institutes of Health and the US Department of Energy, was undertaken with the aim of sequencing the human genome and identifying the genes that contain it. The project was able to identify the locations of many human genes and provide information about their structure and organisation.

According to the Human Genome Project, there are estimated to be over 20,500 human genes. Genome refers to an organisms complete set of DNA, which includes all its genes and mapping these genes simply means finding out the location of these genes in a chromosome.

In humans, each cell consists of 23 pairs of chromosomes for a total of 46 chromosomes, which means that for 23 pairs of chromosomes in each cell, there are roughly 20,500 genes located on them. Some of the genes are lined up in a row on each chromosome, while others are lined up quite close to one another and this arrangement might affect the way they are inherited. For example, if the genes are placed sufficiently close together, there is a probability that they get inherited as a pair.

Genome mapping, therefore, essentially means figuring out the location of a specific gene on a particular region of the chromosome and also determining the location of and relative distances between other genes on that chromosome.

Significantly, genome mapping enables scientists to gather evidence if a disease transmitted from the parent to the child is linked to one or more genes. Furthermore, mapping also helps in determining the particular chromosome which contains that gene and the location of that gene in the chromosome.

According to the National Human Genome Research Institute (NHGRI), genome maps have been used to find out genes that are responsible for relatively rare, single-gene inherited disorders such as cystic fibrosis and Duchenne muscular dystrophy. Genetic maps may also point out scientists to the genes that play a role in more common disorders and diseases such as asthma, cancer and heart disease among others. For instance, in a series of papers published in the journal Nature on Wednesday, researchers from several international institutions mapped the handful of genes whose mutation causes several different kinds of cancers.

According to the Genome News Network, unlike conventional geographical maps, genome maps are one-dimensional, much like the DNA molecules that make up the genome.

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Explained: What is genome mapping? - The Indian Express