Large scale clustering of protein sequences with FORCE -A layout based heuristic for weighted cluster editing

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/.

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Analysis of Large-Scale Human Protein Sequences Using an Efficient Spark-Based DBSCAN Algorithm

Advances in Intelligent Systems and Computing - Proceedings of International Conference on Frontiers in Computing and Systems ◽

10.1007/978-981-15-7834-2_56 ◽

2020 ◽

pp. 601-609

Author(s):

Soumyendu Sekhar Bandyopadhyay ◽

Anup Kumar Halder ◽

Piyali Chatterjee ◽

Jacek Sroka ◽

Mita Nasipuri ◽

...

Keyword(s):

Large Scale ◽

Protein Sequences ◽

Human Protein ◽

Dbscan Algorithm

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The Bologna Annotation Resource: a Non Hierarchical Method for the Functional and Structural Annotation of Protein Sequences Relying on a Comparative Large-Scale Genome Analysis

Journal of Proteome Research ◽

10.1021/pr900204r ◽

2009 ◽

Vol 8 (9) ◽

pp. 4362-4371 ◽

Cited By ~ 6

Author(s):

Lisa Bartoli ◽

Ludovica Montanucci ◽

Raffaele Fronza ◽

Pier Luigi Martelli ◽

Piero Fariselli ◽

...

Keyword(s):

Genome Analysis ◽

Large Scale ◽

Protein Sequences ◽

Structural Annotation ◽

Hierarchical Method

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RECOGNITION OF TRANSMEMBRANE SEGMENTS IN PROTEINS: REVIEW AND CONSISTENCY-BASED BENCHMARKING OF INTERNET SERVERS

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720006002326 ◽

2006 ◽

Vol 04 (05) ◽

pp. 1033-1056 ◽

Cited By ~ 9

Author(s):

NATALIYA S. SADOVSKAYA ◽

ROMAN A. SUTORMIN ◽

MIKHAIL S. GELFAND

Keyword(s):

Membrane Proteins ◽

Genome Annotation ◽

Large Scale ◽

Protein Sequences ◽

Transmembrane Segments ◽

Enzyme Complexes

Membrane proteins perform a number of crucial functions as transporters, receptors, and components of enzyme complexes. Identification of membrane proteins and prediction of their topology is thus an important part of genome annotation. We present here an overview of transmembrane segments in protein sequences, summarize data from large-scale genome studies, and report results of benchmarking of several popular internet servers.

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Large-scale sequence comparisons with sourmash

10.1101/687285 ◽

2019 ◽

Author(s):

N. Tessa Pierce ◽

Luiz Irber ◽

Taylor Reiter ◽

Phillip Brooks ◽

C. Titus Brown

Keyword(s):

Software Package ◽

Large Scale ◽

Sequence Similarity ◽

Protein Sequences ◽

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Sequence Comparisons ◽

Large Databases ◽

Scale Sequence

The sourmash software package uses MinHash-based sketching to create “signatures”, compressed representations of DNA, RNA, and protein sequences, that can be stored, searched, explored, and taxonomically annotated. sourmash signatures can be used to estimate sequence similarity between very large data sets quickly and in low memory, and can be used to search large databases of genomes for matches to query genomes and metagenomes. sourmash is implemented in C++, Rust, and Python, and is freely available under the BSD license at http://github.com/dib-lab/sourmash.

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Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning

10.1101/2020.12.21.423785 ◽

2020 ◽

Author(s):

Jonathan Frazer ◽

Pascal Notin ◽

Mafalda Dias ◽

Aidan Gomez ◽

Kelly Brock ◽

...

Keyword(s):

Genetic Variants ◽

Large Scale ◽

Protein Sequences ◽

Evolutionary Model ◽

Generative Models ◽

Evolutionary Information ◽

Disease Genes ◽

Independent Evidence ◽

Variants Of Unknown Significance ◽

Protein Variants

AbstractQuantifying the pathogenicity of protein variants in human disease-related genes would have a profound impact on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences1–3. In principle, computational methods could support the large-scale interpretation of genetic variants. However, prior methods4–7 have relied on training machine learning models on available clinical labels. Since these labels are sparse, biased, and of variable quality, the resulting models have been considered insufficiently reliable8. By contrast, our approach leverages deep generative models to predict the clinical significance of protein variants without relying on labels. The natural distribution of protein sequences we observe across organisms is the result of billions of evolutionary experiments9,10. By modeling that distribution, we implicitly capture constraints on the protein sequences that maintain fitness. Our model EVE (Evolutionary model of Variant Effect) not only outperforms computational approaches that rely on labelled data, but also performs on par, if not better than, high-throughput assays which are increasingly used as strong evidence for variant classification11–23. After thorough validation on clinical labels, we predict the pathogenicity of 11 million variants across 1,081 disease genes, and assign high-confidence reclassification for 72k Variants of Unknown Significance8. Our work suggests that models of evolutionary information can provide a strong source of independent evidence for variant interpretation and that the approach will be widely useful in research and clinical settings.

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Large scale hierarchical clustering of protein sequences

10.15771/1471-2105_2005_6_15 ◽

2007 ◽

Author(s):

Antje Krause ◽

Jens Stoye ◽

Martin Vingron

Keyword(s):

Hierarchical Clustering ◽

Large Scale ◽

Protein Sequences

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Large-scale sequence comparisons with sourmash

F1000Research ◽

10.12688/f1000research.19675.1 ◽

2019 ◽

Vol 8 ◽

pp. 1006 ◽

Cited By ~ 18

Author(s):

N. Tessa Pierce ◽

Luiz Irber ◽

Taylor Reiter ◽

Phillip Brooks ◽

C. Titus Brown

Keyword(s):

Software Package ◽

Large Scale ◽

Sequence Similarity ◽

Protein Sequences ◽

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Sequence Comparisons ◽

Large Databases ◽

Scale Sequence

The sourmash software package uses MinHash-based sketching to create “signatures”, compressed representations of DNA, RNA, and protein sequences, that can be stored, searched, explored, and taxonomically annotated. sourmash signatures can be used to estimate sequence similarity between very large data sets quickly and in low memory, and can be used to search large databases of genomes for matches to query genomes and metagenomes. sourmash is implemented in C++, Rust, and Python, and is freely available under the BSD license at http://github.com/dib-lab/sourmash.

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Drug-Target Interaction Prediction Based on Drug Fingerprint Information and Protein Sequence

Molecules ◽

10.3390/molecules24162999 ◽

2019 ◽

Vol 24 (16) ◽

pp. 2999 ◽

Cited By ~ 4

Author(s):

Yang Li ◽

Yu-An Huang ◽

Zhu-Hong You ◽

Li-Ping Li ◽

Zheng Wang

Keyword(s):

Drug Target ◽

Large Scale ◽

Protein Sequences ◽

Performance Comparison ◽

Target Pair ◽

Evolutionary Information ◽

Support Vector ◽

Sequence Information ◽

Rotation Forest ◽

Comparison Results

The identification of drug-target interactions (DTIs) is a critical step in drug development. Experimental methods that are based on clinical trials to discover DTIs are time-consuming, expensive, and challenging. Therefore, as complementary to it, developing new computational methods for predicting novel DTI is of great significance with regards to saving cost and shortening the development period. In this paper, we present a novel computational model for predicting DTIs, which uses the sequence information of proteins and a rotation forest classifier. Specifically, all of the target protein sequences are first converted to a position-specific scoring matrix (PSSM) to retain evolutionary information. We then use local phase quantization (LPQ) descriptors to extract evolutionary information in the PSSM. On the other hand, substructure fingerprint information is utilized to extract the features of the drug. We finally combine the features of drugs and protein together to represent features of each drug-target pair and use a rotation forest classifier to calculate the scores of interaction possibility, for a global DTI prediction. The experimental results indicate that the proposed model is effective, achieving average accuracies of 89.15%, 86.01%, 82.20%, and 71.67% on four datasets (i.e., enzyme, ion channel, G protein-coupled receptors (GPCR), and nuclear receptor), respectively. In addition, we compared the prediction performance of the rotation forest classifier with another popular classifier, support vector machine, on the same dataset. Several types of methods previously proposed are also implemented on the same datasets for performance comparison. The comparison results demonstrate the superiority of the proposed method to the others. We anticipate that the proposed method can be used as an effective tool for predicting drug-target interactions on a large scale, given the information of protein sequences and drug fingerprints.

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