MAGUS: Multiple sequence Alignment using Graph clUStering. [PDF]
Smirnov V, Warnow T.
europepmc +1 more source
Sequence Flow: interactive web application for visualizing partial order alignments
Background Multiple sequence alignment (MSA) has proven extremely useful in computational biology, especially in inferring evolutionary relationships via phylogenetic analysis and providing insight into protein structure and function.
Krzysztof Zdąbłasz+2 more
doaj +1 more source
ProPIP: a tool for progressive multiple sequence alignment with Poisson Indel Process. [PDF]
Maiolo M+5 more
europepmc +1 more source
PacRAT: a program to improve barcode-variant mapping from PacBio long reads using multiple sequence alignment. [PDF]
Yeh CC+3 more
europepmc +1 more source
Introduction to Multiple Sequence Alignment
An introduction to multiple sequence alignments (MSAs) for bench biologists delivered as part of the EMBL Australia Masterclass on Protein Sequence Analysis http://oz-masterclass.wikispaces.com/ . Focuses on describing: the "anatomy" of a sequence alignment; two alternative interpretations of alignmetns (structural and evolutionary), and ways of ...
openaire +2 more sources
Relative model selection of evolutionary substitution models can be sensitive to multiple sequence alignment uncertainty. [PDF]
Spielman SJ, Miraglia ML.
europepmc +1 more source
Scalable long read self-correction and assembly polishing with multiple sequence alignment. [PDF]
Morisse P+4 more
europepmc +1 more source
CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction. [PDF]
Ju F+6 more
europepmc +1 more source
Corrigendum: A Detailed View of KIR Haplotype Structures and Gene Families as Provided by a New Motif-Based Multiple Sequence Alignment. [PDF]
Roe D+6 more
europepmc +1 more source
Deep Time Warping for Multiple Time Series Alignment [PDF]
Time Series Alignment is a critical task in signal processing with numerous real-world applications. In practice, signals often exhibit temporal shifts and scaling, making classification on raw data prone to errors. This paper introduces a novel approach for Multiple Time Series Alignment (MTSA) leveraging Deep Learning techniques.
arxiv