scholarly journals Trends in structural coverage of the protein universe and the impact of the Protein Structure Initiative

2014 ◽  
Vol 111 (10) ◽  
pp. 3733-3738 ◽  
Author(s):  
Kamil Khafizov ◽  
Carlos Madrid-Aliste ◽  
Steven C. Almo ◽  
Andras Fiser
Keyword(s):  
Protein Structure ◽  
Structural Genomics ◽  
Large Scale ◽  
Sequence Data ◽  
Taxonomic Composition ◽  
Protein Structure Initiative ◽  
Protein Sequence Data ◽  
Protein Universe ◽  
Structural Coverage ◽  
The Impact

The exponential growth of protein sequence data provides an ever-expanding body of unannotated and misannotated proteins. The National Institutes of Health-supported Protein Structure Initiative and related worldwide structural genomics efforts facilitate functional annotation of proteins through structural characterization. Recently there have been profound changes in the taxonomic composition of sequence databases, which are effectively redefining the scope and contribution of these large-scale structure-based efforts. The faster-growing bacterial genomic entries have overtaken the eukaryotic entries over the last 5 y, but also have become more redundant. Despite the enormous increase in the number of sequences, the overall structural coverage of proteins—including proteins for which reliable homology models can be generated—on the residue level has increased from 30% to 40% over the last 10 y. Structural genomics efforts contributed ∼50% of this new structural coverage, despite determining only ∼10% of all new structures. Based on current trends, it is expected that ∼55% structural coverage (the level required for significant functional insight) will be achieved within 15 y, whereas without structural genomics efforts, realizing this goal will take approximately twice as long.

scholarly journals Protein Structure Initiative Material Repository: an open shared public resource of structural genomics plasmids for the biological community

2009 ◽  
Vol 38 (suppl_1) ◽  
pp. D743-D749 ◽  
Author(s):  
Catherine Y. Cormier ◽  
Stephanie E. Mohr ◽  
Dongmei Zuo ◽  
Yanhui Hu ◽  
Andreas Rolfs ◽  
...  
Keyword(s):  
Protein Structure ◽  
Structural Genomics ◽  
Protein Structure Initiative ◽  
Biological Community ◽  
Public Resource

scholarly journals The Protein Structure Initiative Structural Genomics Knowledgebase

2009 ◽  
Vol 23 (S1) ◽  
Author(s):  
Margaret Gabanyi ◽  
John Westbrook ◽  
Wendy Tao ◽  
Raship Shah ◽  
Andrei Kouranov ◽  
...  
Keyword(s):  
Protein Structure ◽  
Structural Genomics ◽  
Protein Structure Initiative

Glutton: large-scale integration of non-model organism transcriptome data for comparative analysis

2016 ◽  
Author(s):  
Alan Medlar ◽  
Laura Laakso ◽  
Andreia Miraldo ◽  
Ari Löytynoja
Keyword(s):  
Comparative Analysis ◽  
Large Scale ◽  
De Novo ◽  
Sequence Data ◽  
Model Organism ◽  
Model Organisms ◽  
Rna Seq ◽  
Reference Species ◽  
Wide Range ◽  
The Impact

AbstractHigh-throughput RNA-seq data has become ubiquitous in the study of non-model organisms, but its use in comparative analysis remains a challenge. Without a reference genome for mapping, sequence data has to be de novo assembled, producing large numbers of short, highly redundant contigs. Preparing these assemblies for comparative analyses requires the removal of redundant isoforms, assignment of orthologs and converting fragmented transcripts into gene alignments. In this article we present Glutton, a novel tool to process transcriptome assemblies for downstream evolutionary analyses. Glutton takes as input a set of fragmented, possibly erroneous transcriptome assemblies. Utilising phylogeny-aware alignment and reference data from a closely related species, it reconstructs one transcript per gene, finds orthologous sequences and produces accurate multiple alignments of coding sequences. We present a comprehensive analysis of Glutton’s performance across a wide range of divergence times between study and reference species. We demonstrate the impact choice of assembler has on both the number of alignments and the correctness of ortholog assignment and show substantial improvements over heuristic methods, without sacrificing correctness. Finally, using inference of Darwinian selection as an example of downstream analysis, we show that Glutton-processed RNA-seq data give results comparable to those obtained from full length gene sequences even with distantly related reference species. Glutton is available from http://wasabiapp.org/software/glutton/ and is licensed under the GPLv3.

scholarly journals The protein structure initiative structural genomics knowledgebase

2009 ◽  
Vol 37 (Database) ◽  
pp. D365-D368 ◽  
Author(s):  
H. M. Berman ◽  
J. D. Westbrook ◽  
M. J. Gabanyi ◽  
W. Tao ◽  
R. Shah ◽  
...  
Keyword(s):  
Protein Structure ◽  
Structural Genomics ◽  
Protein Structure Initiative

scholarly journals Association Analysis and Meta-Analysis of Multi-allelic Variants for Large Scale Sequence Data

2017 ◽  
Author(s):  
Xiaowei Zhan ◽  
Sai Chen ◽  
Yu Jiang ◽  
Mengzhen Liu ◽  
William G. Iacono ◽  
...  
Keyword(s):  
Large Scale ◽  
Rare Variants ◽  
Sequence Data ◽  
Meta Analysis ◽  
Joint Modeling ◽  
Allelic Variants ◽  
Association Analyses ◽  
Link Type ◽  
Gene Level ◽  
The Impact

AbstractMotivation:There is great interest to understand the impact of rare variants in human diseases using large sequence datasets. In deep sequences datasets of >10,000 samples, ∼10% of the variant sites are observed to be multi-allelic. Many of the multi-allelic variants have been shown to be functional and disease relevant. Proper analysis of multi-allelic variants is critical to the success of a sequencing study, but existing methods do not properly handle multi-allelic variants and can produce highly misleading association results.Results:We propose novel methods to encode multi-allelic sites, conduct single variant and gene-level association analyses, and perform meta-analysis for multi-allelic variants. We evaluated these methods through extensive simulations and the study of a large meta-analysis of ∼18,000 samples on the cigarettes-per-day phenotype. We showed that our joint modeling approach provided an unbiased estimate of genetic effects, greatly improved the power of single variant association tests, and enhanced gene-level tests over existing approaches.Availability:Software packages implementing these methods are available at (https://github.com/zhanxw/rvtestshttp://genome.sph.umich.edu/wiki/RareMETAL).Contact:[email protected]; [email protected]

scholarly journals Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in GenBank

2020 ◽  
Author(s):  
Martin Steinegger ◽  
Steven L Salzberg
Keyword(s):  
Large Scale ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Model Organism ◽  
Taxonomic Composition ◽  
Reference Sequence ◽  
Metagenomic Sequencing ◽  
Protein Database ◽  
Input Size ◽  
Reference Databases

Metagenomic sequencing allows researchers to investigate organisms sampled from their native environments by sequencing their DNA directly, and then quantifying the abundance and taxonomic composition of the organisms thus captured. However, these types of analyses are sensitive to contamination in public databases caused by incorrectly labeled reference sequences. Here we describe Conterminator, an efficient method to detect and remove incorrectly labelled sequences by an exhaustive all-against-all sequence comparison. Our analysis reports contamination in 114,035 sequences and 2767 species in the NCBI Reference Sequence Database (RefSeq), 2,161,746 sequences and 6795 species in the GenBank database, and 14,132 protein sequences in the NR non-redundant protein database. Conterminator uncovers contamination in sequences spanning the whole range from draft genomes to “complete” model organism genomes. Our method, which scales linearly with input size, was able to process 3.3 terabytes of genomic sequence data in 12 days on a single 32-core compute node. We believe that Conterminator can become an important tool to ensure the quality of reference databases with particular importance for downstream metagenomic analyses. Source code (GPLv3): https://github.com/martin-steinegger/conterminator

NMR in structural genomics to increase structural coverage of the protein universe

2021 ◽  
pp. 143-154
Author(s):  
Pedro Serrano ◽  
Samit K. Dutta ◽  
Andrew Proudfoot ◽  
Biswaranjan Mohanty ◽  
Lukas Susac ◽  
...  
Keyword(s):  
Structural Genomics ◽  
Protein Universe ◽  
Structural Coverage
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