使用系统发育感知轮廓的超大比对
Ultra-large alignments using Phylogeny-aware Profiles
摘要：
许多生物学问题，包括深层进化历史的估计和蛋白质序列之间的远程同源性的检测，依赖于多个序列比对（MSA）和大型数据集的系统发育树。然而，精确的大规模多序列比对是非常困难的，特别是当数据集包含片段序列时。我们提出了UPP，MSA方法，使用一种新的机器学习技术-隐马尔可夫模型的包围-我们在这里提出。UPP可对核苷酸和氨基酸序列进行高度准确的比对，即使在超大数据集或包含片段序列的数据集上也是如此。UPP可在https://github.com/smirarab/sepp上获得。
英文摘要：
Many biological questions, including the estimation of deep evolutionary histories and the detection of remote homology between protein sequences, rely upon multiple sequence alignments (MSAs) and phylogenetic trees of large datasets. However, accurate large-scale multiple sequence alignment is very difficult, especially when the dataset contains fragmentary sequences. We present UPP, an MSA method that uses a new machine learning technique - the Ensemble of Hidden Markov Models - that we propose here. UPP produces highly accurate alignments for both nucleotide and amino acid sequences, even on ultra-large datasets or datasets containing fragmentary sequences. UPP is available at https://github.com/smirarab/sepp.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
