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

Author(s):  
Soumyendu Sekhar Bandyopadhyay ◽  
Anup Kumar Halder ◽  
Piyali Chatterjee ◽  
Jacek Sroka ◽  
Mita Nasipuri ◽  
...  
2017 ◽  
Author(s):  
Morgan N. Price ◽  
Adam P. Arkin

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/.


Author(s):  
Zhenlu Li ◽  
Matthias Buck

Of 20,000 or so canonical human protein sequences, as of July 2020, 6,747 proteins have had their full or partial medium to high-resolution structures determined by x-ray crystallography or other methods. Which of these proteins dominate the protein database (the PDB) and why? In this paper, we list the 272 top protein structures based on the number of their PDB depositions. This set of proteins accounts for more than 40% of all available human PDB entries and represent past trend and current status for protein science. We briefly discuss the relationship which some of the prominent protein structures have with protein biophysics research and mention their relevance to human diseases. The information may inspire researchers who are new to protein science, but it also provides a year 2020 snap-shot for the state of protein science.


2021 ◽  
Vol 7 ◽  
Author(s):  
Castrense Savojardo ◽  
Matteo Manfredi ◽  
Pier Luigi Martelli ◽  
Rita Casadio

Solvent accessibility (SASA) is a key feature of proteins for determining their folding and stability. SASA is computed from protein structures with different algorithms, and from protein sequences with machine-learning based approaches trained on solved structures. Here we ask the question as to which extent solvent exposure of residues can be associated to the pathogenicity of the variation. By this, SASA of the wild-type residue acquires a role in the context of functional annotation of protein single-residue variations (SRVs). By mapping variations on a curated database of human protein structures, we found that residues targeted by disease related SRVs are less accessible to solvent than residues involved in polymorphisms. The disease association is not evenly distributed among the different residue types: SRVs targeting glycine, tryptophan, tyrosine, and cysteine are more frequently disease associated than others. For all residues, the proportion of disease related SRVs largely increases when the wild-type residue is buried and decreases when it is exposed. The extent of the increase depends on the residue type. With the aid of an in house developed predictor, based on a deep learning procedure and performing at the state-of-the-art, we are able to confirm the above tendency by analyzing a large data set of residues subjected to variations and occurring in some 12,494 human protein sequences still lacking three-dimensional structure (derived from HUMSAVAR). Our data support the notion that surface accessible area is a distinguished property of residues that undergo variation and that pathogenicity is more frequently associated to the buried property than to the exposed one.


2020 ◽  
Vol 48 (W1) ◽  
pp. W154-W161
Author(s):  
Nawar Malhis ◽  
Matthew Jacobson ◽  
Steven J M Jones ◽  
Jörg Gsponer

Abstract The separation of deleterious from benign mutations remains a key challenge in the interpretation of genomic data. Computational methods used to sort mutations based on their potential deleteriousness rely largely on conservation measures derived from sequence alignments. Here, we introduce LIST-S2, a successor to our previously developed approach LIST, which aims to exploit local sequence identity and taxonomy distances in quantifying the conservation of human protein sequences. Unlike its predecessor, LIST-S2 is not limited to human sequences but can assess conservation and make predictions for sequences from any organism. Moreover, we provide a web-tool and downloadable software to compute and visualize the deleteriousness of mutations in user-provided sequences. This web-tool contains an HTML interface and a RESTful API to submit and manage sequences as well as a browsable set of precomputed predictions for a large number of UniProtKB protein sequences of common taxa. LIST-S2 is available at: https://list-s2.msl.ubc.ca/


Cell ◽  
2009 ◽  
Vol 136 (2) ◽  
pp. 352-363 ◽  
Author(s):  
Alastair J. Barr ◽  
Emilie Ugochukwu ◽  
Wen Hwa Lee ◽  
Oliver N.F. King ◽  
Panagis Filippakopoulos ◽  
...  

2009 ◽  
Vol 8 (9) ◽  
pp. 4362-4371 ◽  
Author(s):  
Lisa Bartoli ◽  
Ludovica Montanucci ◽  
Raffaele Fronza ◽  
Pier Luigi Martelli ◽  
Piero Fariselli ◽  
...  

2006 ◽  
Vol 04 (05) ◽  
pp. 1033-1056 ◽  
Author(s):  
NATALIYA S. SADOVSKAYA ◽  
ROMAN A. SUTORMIN ◽  
MIKHAIL S. GELFAND

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.


2011 ◽  
Vol 301-303 ◽  
pp. 1133-1138 ◽  
Author(s):  
Yan Xiang Fu ◽  
Wei Zhong Zhao ◽  
Hui Fang Ma

Data clustering has been received considerable attention in many applications, such as data mining, document retrieval, image segmentation and pattern classification. The enlarging volumes of information emerging by the progress of technology, makes clustering of very large scale of data a challenging task. In order to deal with the problem, more researchers try to design efficient parallel clustering algorithms. In this paper, we propose a parallel DBSCAN clustering algorithm based on Hadoop, which is a simple yet powerful parallel programming platform. The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware.


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