Biology subjects

Grant, C. E.

Publications and source records attributed to Grant, C. E..

7 recordsLinked to original sources

bioRxiv2025

Label-free quantification in the Crux toolkit

Ultimately, most tandem mass spectrometry (MS/MS) proteomics experiments aim to not just detect but also quantify the proteins in a given complex sample. Here, we describe an extension to the Crux MS/MS analysis toolkit to enable label-free quantification of peptides. We demonstrate that Cruxs new quantification command, which is modeled after the algorithms implemented in the widely used FlashLFQ software, is both efficient and accurate. In particular, we achieve a 1.9-fold speedup while reducing the memory usage by 26%. The new crux-lfq command is available in Crux v4.3.

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bioRxiv2025

Fast and memory efficient searching of large-scale mass spectrometry data using Tide

Over the past 30 years, software for searching tandem mass spectrometry data against a protein database has improved dramatically in speed and statistical power. However, existing tools can still struggle to analyze truly massive datasets, when either the number of spectra or the number of proteins being analyzed grows too large. Here we describe enhancements to the Tide search engine that allow it to handle datasets containing >10 million spectra and databases containing >7 billion peptides on commodity hardware. We demonstrate that the new Tide architecture is around 2-7 times faster than the previous version and is now comparable to MSFragger and Sage in speed while requiring much less memory. Tide is open source and is publicly available as pre-compiled binaries for Windows, Linux and Mac.

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bioRxiv2022

The Crux toolkit for analysis of bottom-up tandem mass spectrometry proteomics data

The Crux tandem mass spectrometry data analysis toolkit provides a collection of algorithms for analyzing bottom-up proteomics tandem mass spectrometry data. Many publications have described various individual components of Crux, but a comprehensive summary has not been published since 2014. The goal of this work is to summarize the functionality of Crux, focusing on developments since 2014. We begin with empirical results demonstrating our recently implemented speedups to the Tide search engine. Other new features include a new score function in Tide, two new confidence estimation procedures, as well as three new tools: Param-medic for estimating search parameters directly from mass spectrometry data, Kojak for searching cross-linked mass spectra, and DIAmeter for searching data independent acquisition data against a sequence database.

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bioRxiv2021

XSTREME: Comprehensive motif analysis of biological sequence datasets

XSTREME is a web-based tool for performing comprehensive motif discovery and analysis in DNA, RNA or protein sequences, as well as in sequences in user-defined alphabets. It is designed for both very large and very small datasets. XSTREME is similar to the MEME-ChIP tool, but expands upon its capabilities in several ways. Like MEME-ChIP, XSTREME performs two types of de novo motif discovery, and also performs motif enrichment analysis of the input sequences using databases of known motifs. Unlike MEME-ChIP, which ranks motifs based on their enrichment in the centers of the input sequences, XSTREME uses enrichment anywhere in the sequences for this purpose. Consequently, XSTREME is more appropriate for motif-based analysis of sequences regardless of how the motifs are distributed within the sequences. XSTREME uses the MEME and STREME algorithms for motif discovery, and the recently developed SEA algorithm for motif enrichment analysis. The interactive HTML output produced by XSTREME includes highly accurate motif significance estimates, plots of the positional distribution of each motif, and histograms of the number of motif matches in each sequences. XSTREME is easy to use via its web server at https://meme-suite.org, and is fully integrated with the widely-used MEME Suite of sequence analysis tools, which can be freely downloaded at the same web site for non-commercial use.

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bioRxiv2021

SEA: Simple Enrichment Analysis of motifs

Motif enrichment algorithms can identify known sequence motifs that are present to a statistically significant degree in DNA, RNA and protein sequences. Databases of such known motifs exist for DNA- and RNA-binding proteins, as well as for many functional protein motifs. The SEA ("Simple Enrichment Analysis") algorithm presented here uses a simple, consistent approach for detecting the enrichment of motifs in DNA, RNA or protein sequences, as well as in sequences using user-defined alphabets. SEA can identify known motifs that are enriched in a single set of input sequences, and can also perform differential motif enrichment analysis when presented with an additional set of control sequences. Using in vivo DNA (ChIP-seq) data as input to SEA, and validating motifs with reference motifs derived from in vitro data, we show that SEA is is faster than three widely-used motif enrichment algorithms (AME, CentriMo and Pscan), while delivering comparable accuracy. We also show that, in contrast to other motif enrichment algorithms, SEA reports accurate estimates of statistical significance. SEA is easy to use via its web server at https://meme-suite.org, and is fully integrated with the widely-used MEME Suite of sequence analysis tools, which can be freely downloaded at the same web site for non-commercial use.

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bioRxiv2019

T-Gene: Improved target gene prediction

MotivationIdentifying the genes regulated by a given transcription factor (its \"target genes\") is a key step in developing a comprehensive understanding of gene regulation. Previously we developed a method for predicting the target genes of a transcription factor (TF) based solely on the correlation between a histone modification at the TFs binding site and the expression of the gene across a set of tissues. That approach is limited to organisms for which extensive histone and expression data is available, and does not explicitly incorporate the genomic distance between the TF and the gene.\n\nResultsWe present the T-Gene algorithm, which overcomes these limitations. T-Gene can be used to predict which genes are most likely to be regulated by a TF, and which of the TFs binding sites are most likely involved in regulating particular genes. T-Gene calculates a novel score that combines distance and histone/expression correlation, and we show that this score accurately predicts when a regulatory element bound by a TF is in contact with a genes promoter, achieving median positive predictive value (PPV) above 50%. T-Gene is easy to use via its web server or as a command-line tool, and can also make accurate predictions (median PPV above 40%) based on distance alone when extensive histone/expression data is not available for the organism. T-Gene provides an estimate of the statistical significance of each of its predictions.\n\nAvailabilityThe T-Gene web server, source code, histone/expression data and genome annotation files are provided at http://meme-suite.org.\n\nContacttimothybailey@unr.edu

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bioRxiv2018

MoMo: Discovery of statistically significant post-translational modification motifs

MotivationPost-translational modifications (PTMs) of proteins are associated with many significant biological functions and can be identified in high throughput using tandem mass spectrometry. Many PTMs are associated with short sequence patterns called \"motifs\" that help localize the modifying enzyme. Accordingly, many algorithms have been designed to identify these motifs from mass spectrometry data. Accurate statistical confidence estimates for discovered motifs are critically important for proper interpretation and in the design of downstream experimental validation.\n\nResultsWe describe a method for assigning statistical confidence estimates to PTM motifs, and we demonstrate that this method provides accurate p-values on both simulated and real data. Our methods are implemented in MoMo, a software tool for discovering motifs among sets of PTMs that we make available as a web server and as downloadable source code. MoMo reimplements the two most widely used PTM motif discovery algorithms--motif-x and MoDL--while offering many enhancements. Relative to motif-x, MoMo offers improved statistical confidence estimates and more accurate calculation of motif scores. The MoMo web server offers more proteome databases, more input formats, larger inputs and longer running times than the motif-x web server. Finally, our study demonstrates that the confidence estimates produced by motif-x are inaccurate. This inaccuracy stems in part from the common practice of drawing \"background\" peptides from an unshuffled proteome database. Our results thus suggest that many of the hundreds of papers that use motif-x to find motifs may be reporting results that lack statistical support.\n\nAvailabilityhttp://meme-suite.org\n\nContacttimothybailey@unr.edu

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