Fabian Sievers

Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega

Sequence embedding for fast construction of guide trees for multiple sequence alignment

Abstract Background The most widely used multiple sequence alignment methods require sequences to be clustered as an initial step. Most sequence clustering methods require a ful...

Simple chained guide trees give high-quality protein multiple sequence alignments

Significance To make sense of protein sequences, they need to be compared with each other. It is common to make a multiple sequence alignment where gaps are inserted to line up ...

Protein multiple sequence alignment benchmarking through secondary structure prediction

Abstract Motivation Multiple sequence alignment (MSA) is commonly used to analyze sets of homologous protein or DNA sequences. This has lead to the development of many methods a...

Using <i>de novo</i> protein structure predictions to measure the quality of very large multiple sequence alignments

Abstract Motivation: Multiple sequence alignments (MSAs) with large numbers of sequences are now commonplace. However, current multiple alignment benchmarks are ill-suited for t...

Systematic exploration of guide-tree topology effects for small protein alignments