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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Benchmarking homology detection procedures with low complexity filters.

Kristoffer Forslund¹, Erik L L Sonnhammer

¹Stockholm Bioinformatics Center, Stockholm University, SE-10691 Stockholm, Sweden. kristoffer.forslund@sbc.su.se

Bioinformatics (Oxford, England)

|July 22, 2009

Summary

Comparing protein homology detection methods, score matrix adjustment offers a low false positive rate with minimal sensitivity loss. MSPcrunch shows less sensitivity loss but a higher false positive rate, with score matrix methods sometimes truncating alignments.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Low-complexity sequence regions pose challenges in identifying true protein homologs.
Existing homology detection benchmarks lack sufficient low-complexity sequences.
A new benchmarking strategy using Pfam domains on whole-proteome data is introduced.

Purpose of the Study:

To evaluate built-in BLAST low complexity filter settings and MSPcrunch post-processing filters.
To compare the effectiveness of different filtering methods on challenging datasets with low-complexity sequences.
To assess the impact of filtering on alignment length.

Main Methods:

Developed an alternative benchmarking strategy using Pfam domains and clans on whole-proteome datasets.
Evaluated six built-in BLAST low complexity filter settings.
Assessed various settings of the MSPcrunch post-processing filter.

Main Results:

Score matrix adjustment methods yielded a low false positive rate with a small loss in sensitivity.
MSPcrunch demonstrated less sensitivity loss but a higher false positive rate.
Alignment truncation was observed as a drawback for score matrix adjustment methods.

Conclusions:

Score matrix adjustment and MSPcrunch offer viable solutions for homology detection in the presence of low-complexity sequences.
The choice between methods depends on the acceptable trade-off between false positive rate and sensitivity.
The developed benchmark provides a realistic assessment of filtering strategies.