scholarly journals PaperBLAST: Text-mining papers for information about homologs

2017 ◽  
Author(s):  
Morgan N. Price ◽  
Adam P. Arkin
Keyword(s):  
Text Mining ◽  
Genome Sequencing ◽  
Full Text ◽  
Large Scale ◽  
Scientific Literature ◽  
Protein Sequences ◽  
Protein Coding ◽  
Link Protein ◽  
Protein Coding Genes ◽  
Link Type

AbstractLarge-scale genome sequencing has identified millions of protein-coding genes whose function is unknown. Many of these proteins are similar to characterized proteins from other organisms, but much of this information is missing from annotation databases and is hidden in the scientific literature. To make this information accessible, PaperBLAST uses EuropePMC to search the full text of scientific articles for references to genes. PaperBLAST also takes advantage of curated resources that link protein sequences to scientific articles (Swiss-Prot, GeneRIF, and EcoCyc). PaperBLAST’s database includes over 700,000 scientific articles that mention over 400,000 different proteins. Given a protein of interest, PaperBLAST quickly finds similar proteins that are discussed in the literature and presents snippets of text from relevant articles or from the curators. PaperBLAST is available at http://papers.genomics.lbl.gov/.

scholarly journals PaperBLAST: Text Mining Papers for Information about Homologs

mSystems ◽  
2017 ◽  
Vol 2 (4) ◽  
Author(s):  
Morgan N. Price ◽  
Adam P. Arkin
Keyword(s):  
Genome Sequencing ◽  
Full Text ◽  
Large Scale ◽  
Protein Sequences ◽  
Similar Function ◽  
Sequencing Data ◽  
Protein Coding ◽  
Link Protein ◽  
Or Gene ◽  
Standard Software

ABSTRACT With the recent explosion of genome sequencing data, there are now millions of uncharacterized proteins. If a scientist becomes interested in one of these proteins, it can be very difficult to find information as to its likely function. Often a protein whose sequence is similar, and which is likely to have a similar function, has been studied already, but this information is not available in any database. To help find articles about similar proteins, PaperBLAST searches the full text of scientific articles for protein identifiers or gene identifiers, and it links these articles to protein sequences. Then, given a protein of interest, it can quickly find similar proteins in its database by using standard software (BLAST), and it can show snippets of text from relevant papers. We hope that PaperBLAST will make it easier for biologists to predict proteins’ functions. Large-scale genome sequencing has identified millions of protein-coding genes whose function is unknown. Many of these proteins are similar to characterized proteins from other organisms, but much of this information is missing from annotation databases and is hidden in the scientific literature. To make this information accessible, PaperBLAST uses EuropePMC to search the full text of scientific articles for references to genes. PaperBLAST also takes advantage of curated resources (Swiss-Prot, GeneRIF, and EcoCyc) that link protein sequences to scientific articles. PaperBLAST’s database includes over 700,000 scientific articles that mention over 400,000 different proteins. Given a protein of interest, PaperBLAST quickly finds similar proteins that are discussed in the literature and presents snippets of text from relevant articles or from the curators. PaperBLAST is available at http://papers.genomics.lbl.gov/ . IMPORTANCE With the recent explosion of genome sequencing data, there are now millions of uncharacterized proteins. If a scientist becomes interested in one of these proteins, it can be very difficult to find information as to its likely function. Often a protein whose sequence is similar, and which is likely to have a similar function, has been studied already, but this information is not available in any database. To help find articles about similar proteins, PaperBLAST searches the full text of scientific articles for protein identifiers or gene identifiers, and it links these articles to protein sequences. Then, given a protein of interest, it can quickly find similar proteins in its database by using standard software (BLAST), and it can show snippets of text from relevant papers. We hope that PaperBLAST will make it easier for biologists to predict proteins’ functions.

scholarly journals Discovering long noncoding RNA predictors of anticancer drug sensitivity beyond protein-coding genes

2019 ◽  
Vol 116 (44) ◽  
pp. 22020-22029 ◽  
Author(s):  
Aritro Nath ◽  
Eunice Y. T. Lau ◽  
Adam M. Lee ◽  
Paul Geeleher ◽  
William C. S. Cho ◽  
...  
Keyword(s):  
Anticancer Drug ◽  
Noncoding Rna ◽  
Large Scale ◽  
Drug Response ◽  
Cancer Cell Line ◽  
Systematic Evaluation ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Clinical Biomarkers ◽  
Response Predictors

Large-scale cancer cell line screens have identified thousands of protein-coding genes (PCGs) as biomarkers of anticancer drug response. However, systematic evaluation of long noncoding RNAs (lncRNAs) as pharmacogenomic biomarkers has so far proven challenging. Here, we study the contribution of lncRNAs as drug response predictors beyond spurious associations driven by correlations with proximal PCGs, tissue lineage, or established biomarkers. We show that, as a whole, the lncRNA transcriptome is equally potent as the PCG transcriptome at predicting response to hundreds of anticancer drugs. Analysis of individual lncRNAs transcripts associated with drug response reveals nearly half of the significant associations are in fact attributable to proximal cis-PCGs. However, adjusting for effects of cis-PCGs revealed significant lncRNAs that augment drug response predictions for most drugs, including those with well-established clinical biomarkers. In addition, we identify lncRNA-specific somatic alterations associated with drug response by adopting a statistical approach to determine lncRNAs carrying somatic mutations that undergo positive selection in cancer cells. Lastly, we experimentally demonstrate that 2 lncRNAs, EGFR-AS1 and MIR205HG, are functionally relevant predictors of anti-epidermal growth factor receptor (EGFR) drug response.

scholarly journals Draft Genome Sequence of Salirhabdus euzebyi Strain Q1438

2020 ◽  
Vol 9 (18) ◽  
Author(s):  
Rita Zgheib ◽  
Hussein Anani ◽  
Didier Raoult ◽  
Pierre-Edouard Fournier
Keyword(s):  
Genome Sequencing ◽  
Genome Sequence ◽  
Bacterial Isolate ◽  
Draft Genome ◽  
Sea Salt ◽  
Whole Genome ◽  
Protein Coding ◽  
Content Type ◽  
Protein Coding Genes ◽  
Rna Genes

In 2007, Salirhabdus euzebyi was first described as a bacterial isolate from a sea salt evaporation pond. As no genome sequence was previously available for this species, we performed whole-genome sequencing. The chromosome of strain Q1438 was 3,784,443 bp long with 36% G+C content, 3,830 protein-coding genes, and 74 RNA genes.

scholarly journals An Exploration of the Sequence of a 2.9-Mb Region of the Genome of Drosophila melanogaster: The Adh Region

Genetics ◽  
1999 ◽  
Vol 153 (1) ◽  
pp. 179-219 ◽  
Author(s):  
M Ashburner ◽  
S Misra ◽  
J Roote ◽  
S E Lewis ◽  
R Blazej ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
Transposable Element ◽  
Large Scale ◽  
Chromosome Region ◽  
Complete Sequence ◽  
Test Methods ◽  
P Element ◽  
Cdna Libraries ◽  
Protein Coding ◽  
Protein Coding Genes

Abstract A contiguous sequence of nearly 3 Mb from the genome of Drosophila melanogaster has been sequenced from a series of overlapping P1 and BAC clones. This region covers 69 chromosome polytene bands on chromosome arm 2L, including the genetically well-characterized “Adh region.” A computational analysis of the sequence predicts 218 protein-coding genes, 11 tRNAs, and 17 transposable element sequences. At least 38 of the protein-coding genes are arranged in clusters of from 2 to 6 closely related genes, suggesting extensive tandem duplication. The gene density is one protein-coding gene every 13 kb; the transposable element density is one element every 171 kb. Of 73 genes in this region identified by genetic analysis, 49 have been located on the sequence; P-element insertions have been mapped to 43 genes. Ninety-five (44%) of the known and predicted genes match a Drosophila EST, and 144 (66%) have clear similarities to proteins in other organisms. Genes known to have mutant phenotypes are more likely to be represented in cDNA libraries, and far more likely to have products similar to proteins of other organisms, than are genes with no known mutant phenotype. Over 650 chromosome aberration breakpoints map to this chromosome region, and their nonrandom distribution on the genetic map reflects variation in gene spacing on the DNA. This is the first large-scale analysis of the genome of D. melanogaster at the sequence level. In addition to the direct results obtained, this analysis has allowed us to develop and test methods that will be needed to interpret the complete sequence of the genome of this species.

scholarly journals SeqSero2: Rapid and Improved Salmonella Serotype Determination Using Whole-Genome Sequencing Data

2019 ◽  
Vol 85 (23) ◽  
Author(s):  
Shaokang Zhang ◽  
Hendrik C. den Bakker ◽  
Shaoting Li ◽  
Jessica Chen ◽  
Blake A. Dinsmore ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Large Scale ◽  
Draft Genome ◽  
The United States ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Link Type

ABSTRACT SeqSero, launched in 2015, is a software tool for Salmonella serotype determination from whole-genome sequencing (WGS) data. Despite its routine use in public health and food safety laboratories in the United States and other countries, the original SeqSero pipeline is relatively slow (minutes per genome using sequencing reads), is not optimized for draft genome assemblies, and may assign multiple serotypes for a strain. Here, we present SeqSero2 (github.com/denglab/SeqSero2; denglab.info/SeqSero2), an algorithmic transformation and functional update of the original SeqSero. Major improvements include (i) additional sequence markers for identification of Salmonella species and subspecies and certain serotypes, (ii) a k-mer based algorithm for rapid serotype prediction from raw reads (seconds per genome) and improved serotype prediction from assemblies, and (iii) a targeted assembly approach for specific retrieval of serotype determinants from WGS for serotype prediction, new allele discovery, and prediction troubleshooting. Evaluated using 5,794 genomes representing 364 common U.S. serotypes, including 2,280 human isolates of 117 serotypes from the National Antimicrobial Resistance Monitoring System, SeqSero2 is up to 50 times faster than the original SeqSero while maintaining equivalent accuracy for raw reads and substantially improving accuracy for assemblies. SeqSero2 further suggested that 3% of the tested genomes contained reads from multiple serotypes, indicating a use for contamination detection. In addition to short reads, SeqSero2 demonstrated potential for accurate and rapid serotype prediction directly from long nanopore reads despite base call errors. Testing of 40 nanopore-sequenced genomes of 17 serotypes yielded a single H antigen misidentification. IMPORTANCE Serotyping is the basis of public health surveillance of Salmonella. It remains a first-line subtyping method even as surveillance continues to be transformed by whole-genome sequencing. SeqSero allows the integration of Salmonella serotyping into a whole-genome-sequencing-based laboratory workflow while maintaining continuity with the classic serotyping scheme. SeqSero2, informed by extensive testing and application of SeqSero in the United States and other countries, incorporates important improvements and updates that further strengthen its application in routine and large-scale surveillance of Salmonella by whole-genome sequencing.

scholarly journals Dissecting Fertility Functions of Drosophila Y Chromosome Genes with CRISPR

Genetics ◽  
2020 ◽  
Vol 214 (4) ◽  
pp. 977-990 ◽  
Author(s):  
Yassi Hafezi ◽  
Samantha R. Sruba ◽  
Steven R. Tarrash ◽  
Mariana F. Wolfner ◽  
Andrew G. Clark
Keyword(s):  
Male Fertility ◽  
Nonsynonymous Substitution ◽  
Wild Type ◽  
Protein Coding ◽  
Repeat Elements ◽  
Protein Coding Genes ◽  
The Third ◽  
Link Type ◽  
Complex Functions ◽  
Lines Of Evidence

Gene-poor, repeat-rich regions of the genome are poorly understood and have been understudied due to technical challenges and the misconception that they are degenerating “junk.” Yet multiple lines of evidence indicate these regions may be an important source of variation that could drive adaptation and species divergence, particularly through regulation of fertility. The ∼40 Mb Y chromosome of Drosophila melanogaster contains only 16 known protein-coding genes, and is highly repetitive and entirely heterochromatic. Most of the genes originated from duplication of autosomal genes and have reduced nonsynonymous substitution rates, suggesting functional constraint. We devised a genetic strategy for recovering and retaining stocks with sterile Y-linked mutations and combined it with CRISPR to create mutants with deletions that disrupt three Y-linked genes. Two genes, PRY and FDY, had no previously identified functions. We found that PRY mutant males are subfertile, but FDY mutant males had no detectable fertility defects. FDY, the newest known gene on the Y chromosome, may have fertility effects that are conditional or too subtle to detect. The third gene, CCY, had been predicted but never formally shown to be required for male fertility. CRISPR targeting and RNA interference of CCY caused male sterility. Surprisingly, however, our CCY mutants were sterile even in the presence of an extra wild-type Y chromosome, suggesting that perturbation of the Y chromosome can lead to dominant sterility. Our approach provides an important step toward understanding the complex functions of the Y chromosome and parsing which functions are accomplished by genes vs. repeat elements.

scholarly journals High‐quality genomes reveal significant genetic divergence and cryptic speciation in the model organism Folsomia candida (Collembola)

2021 ◽  
Author(s):  
Yun-Xia Luan ◽  
Yingying Cui ◽  
Wan-Jun Chen ◽  
Jianfeng Jin ◽  
Ai-Min Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Test Organism ◽  
Gene Families ◽  
Species Differentiation ◽  
Folsomia Candida ◽  
Cryptic Speciation ◽  
High Quality ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Soil Arthropod

The collembolan Folsomia candida Willem, 1902, is an important representative soil arthropod that is widely distributed throughout the world and has been frequently used as a test organism in soil ecology and ecotoxicology studies. However, it is questioned as an ideal “standard” because of differences in reproductive modes and cryptic genetic diversity between strains from various geographical origins. In this study, we present two high-quality chromosome-level genomes of F. candida, for the parthenogenetic Danish strain (FCDK, 219.08 Mb, N50 of 38.47 Mb, 25,139 protein-coding genes) and the sexual Shanghai strain (FCSH, 153.09 Mb, N50 of 25.75 Mb, 21,609 protein-coding genes). The seven chromosomes of FCDK are each 25–54% larger than the corresponding chromosomes of FCSH, showing obvious repetitive element expansions and large-scale inversions and translocations but no whole-genome duplication. The strain-specific genes, expanded gene families and genes in nonsyntenic chromosomal regions identified in FCDK are highly related to its broader environmental adaptation. In addition, the overall sequence identity of the two mitogenomes is only 78.2%, and FCDK has fewer strain-specific microRNAs than FCSH. In conclusion, FCDK and FCSH have accumulated independent genetic changes and evolved into distinct species since diverging 10 Mya. Our work shows that F. candida represents a good model of rapidly cryptic speciation. Moreover, it provides important genomic resources for studying the mechanisms of species differentiation, soil arthropod adaptation to soil ecosystems, and Wolbachia-induced parthenogenesis as well as the evolution of Collembola, a pivotal phylogenetic clade between Crustacea and Insecta.

scholarly journals Assembling a phosphoproteomic knowledge base using ProtMapper to normalize phosphosite information from databases and text mining

2019 ◽  
Author(s):  
John A. Bachman ◽  
Benjamin M. Gyori ◽  
Peter K. Sorger
Keyword(s):  
Text Mining ◽  
Open Source ◽  
Large Scale ◽  
Large Body ◽  
Fold Increase ◽  
Human Protein ◽  
Phosphorylation Sites ◽  
Scientific Publications ◽  
Link Type ◽  
Aggregate Information

AbstractA major challenge in analyzing large phosphoproteomic datasets is that information on phosphorylating kinases and other upstream regulators is limited to a small fraction of phosphosites. One approach to addressing this problem is to aggregate and normalize information from all available information sources, including both curated databases and large-scale text mining. However, when we attempted to aggregate information on post-translational modifications (PTMs) from six databases and three text mining systems, we found that a substantial proportion of phosphosites were positioned on non-canonical residue positions. These errors were attributable to the use of residue numbers from non-canonical isoforms, mouse or rat proteins, post-translationally processed proteins and also from errors in curation and text mining. Published mass spectrometry datasets from large-scale efforts such as the Clinical Proteomic Tumor Analysis Consortium (CPTAC) also localize many PTMs to non-canonical sequences, precluding their accurate annotation. To address these problems, we developed ProtMapper, an open-source Python tool that automatically normalizes site positions to human protein reference sequences using data from PhosphoSitePlus and Uniprot. ProtMapper identifies valid reference positions with high precision and reasonable recall, making it possible to filter out machine reading errors from text mining and thereby assemble a corpus of 29,400 regulatory annotations for 13,668 sites, a 2.8-fold increase over PhosphoSitePlus, the current gold standard. To our knowledge this corpus represents the most comprehensive source of literature-derived information about phosphosite regulation currently available and its assembly illustrates the importance of sequence normalization. Combining the expanded corpus of annotations with normalization of CPTAC data nearly doubled the number of CPTAC annotated sites and the mean number of annotations per site. ProtMapper is available under an open source BSD 2-clause license at https://github.com/indralab/protmapper, and the corpus of phosphosite annotations is available as Supplementary Data with this paper under a CC-BY-NC-SA license. All results from the paper are reproducible from code available at https://github.com/johnbachman/protmapper_paper.Author SummaryPhosphorylation is a type of chemical modification that can affect the activity, interactions, or cellular location of proteins. Experimentally measured patterns of protein phosphorylation can be used to infer the mechanisms of cell behavior and disease, but this type of analysis depends on the availability of functional information about the regulation and effects of individual phosphorylation sites. In this study we show that inconsistent descriptions of the physical locations of phosphorylation sites on proteins present a barrier to the functional analysis of phosphorylation data. These inconsistencies are found in both pathway databases and text mining results and often come from the underlying scientific publications. We describe a method to normalize phosphosite locations to standard human protein sequences and use this method to robustly aggregate information from many sources. The result is a large body of functional annotations that increases the proportion of phosphosites with known regulators in two large experimental surveys of phosphorylation in cancer.

scholarly journals Text mining of 15 million full-text scientific articles

2017 ◽  
Author(s):  
David Westergaard ◽  
Hans-Henrik Stærfeldt ◽  
Christian Tønsberg ◽  
Lars Juhl Jensen ◽  
Søren Brunak
Keyword(s):  
Text Mining ◽  
Full Text ◽  
Disease Gene ◽  
Scientific Literature ◽  
Named Entity Recognition ◽  
Recognition System ◽  
Entity Recognition ◽  
Data Sets ◽  
Named Entity ◽  
Benchmark Data

AbstractAcross academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823–2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein–protein, disease–gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only.

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