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Updated: Apr 12, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Software for the analysis and visualization of deep mutational scanning data.

Jesse D Bloom¹

¹Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, 98109, WA, USA. jbloom@fredhutch.org.

BMC Bioinformatics

|May 21, 2015

Summary

This summary is machine-generated.

This study introduces dms_tools, a software package for analyzing deep mutational scanning (DMS) data. It provides more accurate mutation impact inferences than traditional methods, aiding in understanding protein function and evolution.

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Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

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Published on: June 28, 2018

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Deep mutational scanning (DMS) quantifies mutation impacts using deep sequencing and functional selection.
Inferring mutation effects relies on analyzing count changes before and after selection.

Purpose of the Study:

To introduce dms_tools, a novel software package for analyzing DMS data.
To improve the accuracy of inferring mutation impacts from DMS experiments.

Main Methods:

Developed dms_tools, a software package employing a likelihood-based approach for mutation count analysis.
Compared dms_tools' performance against ratio-based methods using simulated data.

Main Results:

dms_tools demonstrated superior accuracy in inferring mutation impacts compared to simple count ratios.
The software enables inference of site-specific amino acid preferences under selection.
It facilitates the assessment of how these preferences shift across different selection pressures.

Conclusions:

dms_tools offers a statistically sound framework for the analysis of DMS data.
The package includes tools for intuitive visualization of mutation preferences and their changes.