SPARK-MSNA: Efficient algorithm on Apache Spark for aligning multiple similar DNA/RNA sequences with supervised learning.

ABSTRACT: Multiple sequence alignment (MSA) is an integral part of molecular biology. But handling massive number of large sequences is still a bottleneck for most of the state-of-the-art software tools. Knowledge driven algorithms utilizing features of input sequences, such as high similarity in case of DNA sequences, can help in improving the efficiency of DNA MSA to assist in phylogenetic tree construction, comparative genomics etc. This article showcases the benefit of utilizing similarity features while performing the alignment. The algorithm uses suffix tree for identifying common substrings and uses a modified Needleman-Wunsch algorithm for pairwise alignments. In order to improve the efficiency of pairwise alignments, a knowledge base is created and a supervised learning with nearest neighbo

SUBMITTER: Vineetha V 

PROVIDER: S-EPMC6488671 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature