PrecisionProDB: improving the proteomics performance for precision medicine

2021 ◽  
Author(s):  
Xiaolong Cao ◽  
Jinchuan Xing
Keyword(s):  
Cell Line ◽  
Perfect Match ◽  
Supplementary Information ◽  
Reference Database ◽  
Specific Reference ◽  
Search Performance ◽  
Protein Database ◽  
Protein Databases ◽  
Genomics And Proteomics ◽  
Reference Protein

Abstract Summary As the next-generation sequencing technology becomes broadly applied, genomics and transcriptomics are becoming more commonly used in both research and clinical settings. However, proteomics is still an obstacle to be conquered. For most peptide search programs in proteomics, a standard reference protein database is used. Because of the thousands of coding DNA variants in each individual, a standard reference database does not provide perfect match for many proteins/peptides of an individual. A personalized reference database can improve the detection power and accuracy for individual proteomics data. To connect genomics and proteomics, we designed a Python package PrecisionProDB that is specialized for generating a personized protein database for proteomics applications. PrecisionProDB supports multiple popular file formats and reference databases, and can generate a personized database in minutes. To demonstrate the application of PrecisionProDB, we generated human population-specific reference protein databases with PrecisionProDB, which improves the number of identified peptides by 0.34% on average. In addition, by incorporating cell line-specific variants into the protein database, we demonstrated a 0.71% improvement for peptide identification in the Jurkat cell line. With PrecisionProDB and these datasets, researchers and clinicians can improve their peptide search performance by adopting the more representative protein database or adding population and individual-specific proteins to the search database with minimum increase of efforts. Availabilityand implementation PrecisionProDB and pre-calculated protein databases are freely available at https://github.com/ATPs/PrecisionProDB and https://github.com/ATPs/PrecisionProDB_references. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Evaluation of bottom-up and top-down mass spectrum identifications with different customized protein sequences databases

2019 ◽  
Author(s):  
Ziwei Li ◽  
Bo He ◽  
Weixing Feng
Keyword(s):  
Mass Spectra ◽  
High Sensitivity ◽  
Supplementary Information ◽  
Reference Database ◽  
Sequence Database ◽  
Top Down ◽  
Protein Database ◽  
Bottom Up ◽  
Genomic Annotation ◽  
Diploid Cells

Abstract Motivation Generally, bottom-up and top-down are two complementary approaches for proteoforms identification. The inference of proteoforms relies on searching mass spectra against an accurate proteoform sequence database. A customized protein sequence database derived by RNA-Seq data can be used to better identify the proteoform existed in a studied species. However, the quality of sequences in customized databases which constructed by different strategies affect the performances of mass spectrometry (MS) identification. Additionally, performances of identifications between bottom-up and top-down using customized databases are also needed to be evaluated Results Three customized databases were constructed with different strategies separately. Two of them were based on translating assembled transcripts with or without genomic annotation, and the third one is a variant-extending protein database. By testing with bottom-up and top-down MS data separately, a variant-extending protein database could identify not only the most number of spectra but also the alleles expressed at the same time in diploid cells. An assembled database could identify the spectrum missed in reference database and amino acid (AA) alterations existed in studied species. Availability and implementation Experimental results demonstrated that the proteoform sequences in an annotated database are more suitable for identifying AA alterations and peptide sequences missed in reference database. An unannotated database instead of a reference proteome database gets an enough high sensitivity of identifying mass spectra. The variant-extending reference database is the most sensitive to identify mass spectra and single AA variants Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Proteogenomics: an analysis of how and when it enhances proteomics

2021 ◽  
Author(s):  
Laura Fancello ◽  
Thomas Burger
Keyword(s):  
Protein Identification ◽  
Control Method ◽  
Peptide Identification ◽  
Reference Database ◽  
Lower Sensitivity ◽  
Protein Databases ◽  
Reference Protein ◽  
Database Size ◽  
Increased Sensitivity ◽  
The Right

ABSTRACTBackgroundProteogenomics aims to identify variant or unknown proteins in bottom-up proteomics, by searching transcriptome- or genome-derived custom protein databases. However, empirical observations reveal that these large proteogenomic databases produce lower-sensitivity peptide identifications. Various strategies have been proposed to avoid this, including the generation of reduced transcriptome-informed protein databases (i.e., built from reference protein databases only retaining proteins whose transcripts are detected in the sample-matched transcriptome), which were found to increase peptide identification sensitivity. Here, we present a detailed evaluation of this approach.ResultsFirst, we established that the increased sensitivity in peptide identification is in fact a statistical artifact, directly resulting from the limited capability of target-decoy competition to accurately model incorrect target matches when using excessively small databases. As anti-conservative FDRs are likely to hamper the robustness of the resulting biological conclusions, we advocate for alternative FDR control methods that are less sensitive to database size. Nevertheless, reduced transcriptome-informed databases are useful, as they reduce the ambiguity of protein identifications, yielding fewer shared peptides. Furthermore, searching the reference database and subsequently filtering proteins whose transcripts are not expressed reduces protein identification ambiguity to a similar extent, but is more transparent and reproducible.ConclusionIn summary, using transcriptome information is an interesting strategy that has not been promoted for the right reasons. While the increase in peptide identifications from searching reduced transcriptome-informed databases is an artifact caused by the use of an FDR control method unsuitable to excessively small databases, transcriptome information can reduce ambiguity of protein identifications.

lncLocator 2.0: a cell-line-specific subcellular localization predictor for long non-coding RNAs with interpretable deep learning

2021 ◽  
Author(s):  
Yang Lin ◽  
Xiaoyong Pan ◽  
Hong-Bin Shen
Keyword(s):  
Subcellular Localization ◽  
Cell Line ◽  
Cell Lines ◽  
Short Term Memory ◽  
Computational Method ◽  
Language Models ◽  
Supplementary Information ◽  
Deep Model ◽  
A Cell ◽  
Non Coding Rnas

Abstract Motivation Long non-coding RNAs (lncRNAs) are generally expressed in a tissue-specific way, and subcellular localizations of lncRNAs depend on the tissues or cell lines that they are expressed. Previous computational methods for predicting subcellular localizations of lncRNAs do not take this characteristic into account, they train a unified machine learning model for pooled lncRNAs from all available cell lines. It is of importance to develop a cell-line-specific computational method to predict lncRNA locations in different cell lines. Results In this study, we present an updated cell-line-specific predictor lncLocator 2.0, which trains an end-to-end deep model per cell line, for predicting lncRNA subcellular localization from sequences.We first construct benchmark datasets of lncRNA subcellular localizations for 15 cell lines. Then we learn word embeddings using natural language models, and these learned embeddings are fed into convolutional neural network, long short-term memory and multilayer perceptron to classify subcellular localizations. lncLocator 2.0 achieves varying effectiveness for different cell lines and demonstrates the necessity of training cell-line-specific models. Furthermore, we adopt Integrated Gradients to explain the proposed model in lncLocator 2.0, and find some potential patterns that determine the subcellular localizations of lncRNAs, suggesting that the subcellular localization of lncRNAs is linked to some specific nucleotides. Availability The lncLocator 2.0 is available at www.csbio.sjtu.edu.cn/bioinf/lncLocator2 and the source code can be found at https://github.com/Yang-J-LIN/lncLocator2. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Scoring functions for drug-effect similarity

2020 ◽  
Author(s):  
Stephan Struckmann ◽  
Mathias Ernst ◽  
Sarah Fischer ◽  
Nancy Mah ◽  
Georg Fuellen ◽  
...  
Keyword(s):  
Cell Line ◽  
Drug Effect ◽  
Pearson Correlation ◽  
Source Code ◽  
New Drugs ◽  
Supplementary Information ◽  
Disease Genes ◽  
Scoring Functions ◽  
Profile Changes

Abstract Motivation The difficulty to find new drugs and bring them to the market has led to an increased interest to find new applications for known compounds. Biological samples from many disease contexts have been extensively profiled by transcriptomics, and, intuitively, this motivates to search for compounds with a reversing effect on the expression of characteristic disease genes. However, disease effects may be cell line-specific and also depend on other factors, such as genetics and environment. Transcription profile changes between healthy and diseased cells relate in complex ways to profile changes gathered from cell lines upon stimulation with a drug. Despite these differences, we expect that there will be some similarity in the gene regulatory networks at play in both situations. The challenge is to match transcriptomes for both diseases and drugs alike, even though the exact molecular pathology/pharmacogenomics may not be known. Results We substitute the challenge to match a drug effect to a disease effect with the challenge to match a drug effect to the effect of the same drug at another concentration or in another cell line. This is welldefined, reproducible in vitro and in silico and extendable with external data. Based on the Connectivity Map (CMap) dataset, we combined 26 different similarity scores with six different heuristics to reduce the number of genes in the model. Such gene filters may also utilize external knowledge e.g. from biological networks. We found that no similarity score always outperforms all others for all drugs, but the Pearson correlation finds the same drug with the highest reliability. Results are improved by filtering for highly expressed genes and to a lesser degree for genes with large fold changes. Also a network-based reduction of contributing transcripts was beneficial, here implemented by the FocusHeuristics. We found no drop in prediction accuracy when reducing the whole transcriptome to the set of 1000 landmark genes of the CMap’s successor project Library of Integrated Network-based Cellular Signatures. All source code to re-analyze and extend the CMap data, the source code of heuristics, filters and their evaluation are available to propel the development of new methods for drug repurposing. Availability https://bitbucket.org/ibima/moldrugeffectsdb Contact [email protected] Supplementary information Supplementary data are available at Briefings in Bioinformatics online.

scholarly journals PHANOTATE: a novel approach to gene identification in phage genomes

2019 ◽  
Vol 35 (22) ◽  
pp. 4537-4542 ◽  
Author(s):  
Katelyn McNair ◽  
Carol Zhou ◽  
Elizabeth A Dinsdale ◽  
Brian Souza ◽  
Robert A Edwards
Keyword(s):  
Gene Prediction ◽  
Optimal Path ◽  
Genome Structure ◽  
Weighted Graph ◽  
Open Reading Frames ◽  
Supplementary Information ◽  
Functional Protein ◽  
Protein Database ◽  
Protein Coding ◽  
Novel Approach

Abstract Motivation Currently there are no tools specifically designed for annotating genes in phages. Several tools are available that have been adapted to run on phage genomes, but due to their underlying design, they are unable to capture the full complexity of phage genomes. Phages have adapted their genomes to be extremely compact, having adjacent genes that overlap and genes completely inside of other longer genes. This non-delineated genome structure makes it difficult for gene prediction using the currently available gene annotators. Here we present PHANOTATE, a novel method for gene calling specifically designed for phage genomes. Although the compact nature of genes in phages is a problem for current gene annotators, we exploit this property by treating a phage genome as a network of paths: where open reading frames are favorable, and overlaps and gaps are less favorable, but still possible. We represent this network of connections as a weighted graph, and use dynamic programing to find the optimal path. Results We compare PHANOTATE to other gene callers by annotating a set of 2133 complete phage genomes from GenBank, using PHANOTATE and the three most popular gene callers. We found that the four programs agree on 82% of the total predicted genes, with PHANOTATE predicting more genes than the other three. We searched for these extra genes in both GenBank’s non-redundant protein database and all of the metagenomes in the sequence read archive, and found that they are present at levels that suggest that these are functional protein-coding genes. Availability and implementation https://github.com/deprekate/PHANOTATE Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen

2019 ◽  
Vol 36 (5) ◽  
pp. 1607-1613 ◽  
Author(s):  
Joseph C Boyd ◽  
Alice Pinheiro ◽  
Elaine Del Nery ◽  
Fabien Reyal ◽  
Thomas Walter
Keyword(s):  
Drug Discovery ◽  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Mechanism Of Action ◽  
Molecular Subtype ◽  
Supplementary Information ◽  
Breast Cancer Cell Lines ◽  
Molecular Heterogeneity ◽  
Drug Mechanism

Abstract Motivation High-content screening is an important tool in drug discovery and characterization. Often, high-content drug screens are performed on one single-cell line. Yet, a single-cell line cannot be thought of as a perfect disease model. Many diseases feature an important molecular heterogeneity. Consequently, a drug may be effective against one molecular subtype of a disease, but less so against another. To characterize drugs with respect to their effect not only on one cell line but on a panel of cell lines is therefore a promising strategy to streamline the drug discovery process. Results The contribution of this article is 2-fold. First, we investigate whether we can predict drug mechanism of action (MOA) at the molecular level without optimization of the MOA classes to the screen specificities. To this end, we benchmark a set of algorithms within a conventional pipeline, and evaluate their MOA prediction performance according to a statistically rigorous framework. Second, we extend this conventional pipeline to the simultaneous analysis of multiple cell lines, each manifesting potentially different morphological baselines. For this, we propose multi-task autoencoders, including a domain-adaptive model used to construct domain-invariant feature representations across cell lines. We apply these methods to a pilot screen of two triple negative breast cancer cell lines as models for two different molecular subtypes of the disease. Availability and implementation https://github.com/jcboyd/multi-cell-line or https://zenodo.org/record/2677923. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Synthetic Sequencing Standards: A Guide to Database Choice for Rumen Microbiota Amplicon Sequencing Analysis

2020 ◽  
Vol 11 ◽  
Author(s):  
Paul E. Smith ◽  
Sinead M. Waters ◽  
Ruth Gómez Expósito ◽  
Hauke Smidt ◽  
Ciara A. Carberry ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Cost Effective ◽  
Amplicon Sequencing ◽  
Gas Production ◽  
Reference Database ◽  
Specific Reference ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Rumen Microbiota ◽  
Reference Databases

Our understanding of complex microbial communities, such as those residing in the rumen, has drastically advanced through the use of high throughput sequencing (HTS) technologies. Indeed, with the use of barcoded amplicon sequencing, it is now cost effective and computationally feasible to identify individual rumen microbial genera associated with ruminant livestock nutrition, genetics, performance and greenhouse gas production. However, across all disciplines of microbial ecology, there is currently little reporting of the use of internal controls for validating HTS results. Furthermore, there is little consensus of the most appropriate reference database for analyzing rumen microbiota amplicon sequencing data. Therefore, in this study, a synthetic rumen-specific sequencing standard was used to assess the effects of database choice on results obtained from rumen microbial amplicon sequencing. Four DADA2 reference training sets (RDP, SILVA, GTDB, and RefSeq + RDP) were compared to assess their ability to correctly classify sequences included in the rumen-specific sequencing standard. In addition, two thresholds of phylogenetic bootstrapping, 50 and 80, were applied to investigate the effect of increasing stringency. Sequence classification differences were apparent amongst the databases. For example the classification of Clostridium differed between all databases, thus highlighting the need for a consistent approach to nomenclature amongst different reference databases. It is hoped the effect of database on taxonomic classification observed in this study, will encourage research groups across various microbial disciplines to develop and routinely use their own microbiome-specific reference standard to validate analysis pipelines and database choice.

scholarly journals JSpeciesWS: a web server for prokaryotic species circumscription based on pairwise genome comparison

2015 ◽  
Vol 32 (6) ◽  
pp. 929-931 ◽  
Author(s):  
Michael Richter ◽  
Ramon Rosselló-Móra ◽  
Frank Oliver Glöckner ◽  
Jörg Peplies
Keyword(s):  
Draft Genome ◽  
Web Server ◽  
Supplementary Information ◽  
Genome Comparison ◽  
Reference Database ◽  
Online Service ◽  
Genome Sequences ◽  
Species Circumscription ◽  
Correlation Search ◽  
User Friendly

Abstract Summary: JSpecies Web Server (JSpeciesWS) is a user-friendly online service for in silico calculating the extent of identity between two genomes, a parameter routinely used in the process of polyphasic microbial species circumscription. The service measures the average nucleotide identity (ANI) based on BLAST+ (ANIb) and MUMmer (ANIm), as well as correlation indexes of tetra-nucleotide signatures (Tetra). In addition, it provides a Tetra Correlation Search function, which allows to rapidly compare selected genomes against a continuously updated reference database with currently about 32 000 published whole and draft genome sequences. For comparison, own genomes can be uploaded and references can be selected from the JSpeciesWS reference database. The service indicates whether two genomes share genomic identities above or below the species embracing thresholds, and serves as a fast way to allocate unknown genomes in the frame of the hitherto sequenced species. Availability and implementation: JSpeciesWS is available at http://jspecies.ribohost.com/jspeciesws. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: [email protected]

scholarly journals Comparison of false-discovery rates of various decoy databases

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Sangjeong Lee ◽  
Heejin Park ◽  
Hyunwoo Kim
Keyword(s):  
Human Protein ◽  
Protein Database ◽  
False Discovery Rates ◽  
Decoy Database ◽  
Protein Databases ◽  
False Discovery ◽  
Large Databases ◽  
Human Protein Database ◽  
Target Database ◽  
Discovery Rates

Abstract Background The target-decoy strategy effectively estimates the false-discovery rate (FDR) by creating a decoy database with a size identical to that of the target database. Decoy databases are created by various methods, such as, the reverse, pseudo-reverse, shuffle, pseudo-shuffle, and the de Bruijn methods. FDR is sometimes over- or under-estimated depending on which decoy database is used because the ratios of redundant peptides in the target databases are different, that is, the numbers of unique (non-redundancy) peptides in the target and decoy databases differ. Results We used two protein databases (the UniProt Saccharomyces cerevisiae protein database and the UniProt human protein database) to compare the FDRs of various decoy databases. When the ratio of redundant peptides in the target database is low, the FDR is not overestimated by any decoy construction method. However, if the ratio of redundant peptides in the target database is high, the FDR is overestimated when the (pseudo) shuffle decoy database is used. Additionally, human and S. cerevisiae six frame translation databases, which are large databases, also showed outcomes similar to that from the UniProt human protein database. Conclusion The FDR must be estimated using the correction factor proposed by Elias and Gygi or that by Kim et al. when (pseudo) shuffle decoy databases are used.

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