Multiple sequence alignment for short sequences
원문 초록
Multiple sequence alignment (MSA) has been one of the most important problems in bioinformatics for more decades and it is still heavily examined by many mathematicians and biologists. However, mostly because of the practical motivation of this problem, the research on this topic is focused on aligning long sequences. It is understandable, since the sequences that need to be aligned (usually DNA or protein sequences) are generally quite long (e. g., at least 30-40 characters). Nevertheless, it is a challenging question that exactly where MSA starts to become a real hard problem (since it is known that MSA is NP-complete [2]), and the key to answer this question is to examine short sequences. If the optimal alignment for short sequences could be determined in polynomial time, then these results may help to develop faster or more accurate heuristic algorithms for aligning long sequences. In this work, it is shown that for length-1 sequences using arbitrary metric, as well as for length-2 sequences using unit metric, the optimum of the MSA problem can be achieved by the trivial alignment.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training
ESM-NBR: fast and accurate nucleic acid-binding residue prediction via protein language model feature representation and multi-task learning
PEvoLM: Protein Sequence Evolutionary Information Language Model
PoET: A generative model of protein families as sequences-of-sequences
Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation
ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis
토론
이 논문과 연결된 포럼 글이 아직 없습니다.