Alignathon: a competitive assessment of whole-genome alignment methods
Dent Earl1, Ngan Nguyen1, Glenn Hickey2
1Center for Biomolecular Science and Engineering, University of California Santa Cruz, Santa Cruz, California 95064, USA; Biomolecular Engineering Department, University of California Santa Cruz, Santa Cruz, California 95064, USA;
Genome Research
|October 3, 2014
Summary
This study benchmarks whole-genome alignment (WGA) tools, revealing significant accuracy differences. Few tools effectively align duplications or perform well at longer evolutionary distances.
Area of Science:
- Computational biology
- Bioinformatics
- Evolutionary genomics
Background:
- Multiple sequence alignments (MSAs) are crucial for evolutionary analyses.
- Existing benchmarks primarily focus on protein and nucleotide MSAs, with limited resources for whole-genome alignment (WGA).
Purpose of the Study:
- To establish benchmarks for whole-genome alignment (WGA) tools.
- To evaluate the accuracy and performance of contemporary WGA pipelines.
Main Methods:
- Organized a competitive evaluation using simulated and real genomic datasets (primate, mammalian, fly).
- Assessed 35 submissions from 10 teams utilizing 12 different alignment pipelines.
- Employed simulation-based and statistical assessment methods for evaluation.
Main Results:
- Identified substantial accuracy variations among WGA tools.
- Observed differences in alignment quality across annotated genomic regions and limited alignment of duplications.
- Found that many tools performed well at shorter evolutionary distances, but fewer at longer distances.
Conclusions:
- Contemporary WGA tools exhibit significant performance disparities.
- Alignment quality is influenced by genomic region and evolutionary distance.
- The study provides valuable datasets and assessment resources for future WGA benchmarking.
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