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Multiple Sequence Alignment(MSA) is a biological concept of aligning three or more sequences. It is of great importance for studying the genetic functions, structures and evolution process of the biological sequences. The aligning of multiple sequences is one of the most fundamental problems in Bioinformatics and it has been widely used for a lot of biological applications like predicting the shapes of proteins, constructing the phylogeny trees, etc . MSAs demand extremely sensitive computational methodologies to come up with precise results. The objective of this project is to conduct a comparative study on various MSA tools on the basis of computation time, accuracy, memory use and sequence length to identify the best use-case for each.
Dynammic programming, classification, clustering, Hidden Markov Models and multiple sequence alignment
Conducted a research case study of comparing and analyzing two of the most widely used Multiple Sequence Alignment algorithms (MUSCLE and CLUSTAL OMEGA) for the study of protein nucleotides. The algorithms were examined and evaluated along the metrics of standard gold benchmark like PREFAB, OXBENCH, and time and space complexities.
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leraning
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network (NIPS 2017)
Template-free prediction of organic reaction outcomes
苗苗 约苗 九价 秒杀 脚本
A collection of tools for Multiple Alignments of DNA, RNA, Protein Sequence
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