Fast discovery and visualization of conserved regions in DNA sequences using quasi-alignment

Abstract

BackgroundNext Generation Sequencing techniques are producing enormous amounts of biological sequence data and analysis becomes a major computational problem. Currently, most analysis, especially the identification of conserved regions, relies heavily on Multiple Sequence Alignment and its various heuristics such as progressive alignment, whose run time grows with the square of the number and the length of the aligned sequences and requires significant computational resources. In this work, we present a method to efficiently discover regions of high similarity across multiple sequences without performing expensive sequence alignment. The method is based on approximating edit distance between segments of sequences using p-mer frequency counts. Then, efficient high-throughput data stream clustering is used to group highly similar segments into so called quasi-alignments. Quasi-alignments have numerous applications such as identifying species and their taxonomic class from sequences, comparing sequences for similarities, and, as in this paper, discovering conserved regions across related sequences.ResultsIn this paper, we show that quasi-alignments can be used to discover highly similar segments across multiple sequences from related or different genomes efficiently and accurately. Experiments on a large number of unaligned 16S rRNA sequences obtained from the Greengenes database show that the method is able to identify conserved regions which agree with known hypervariable regions in 16S rRNA. Furthermore, the experiments show that the proposed method scales well for large data sets with a run time that grows only linearly with the number and length of sequences, whereas for existing multiple sequence alignment heuristics the run time grows super-linearly.ConclusionQuasi-alignment-based algorithms can detect highly similar regions and conserved areas across multiple sequences. Since the run time is linear and the sequences are converted into a compact clustering model, we are able to identify conserved regions fast or even interactively using a standard PC. Our method has many potential applications such as finding characteristic signature sequences for families of organisms and studying conserved and variable regions in, for example, 16S rRNA.

SMYD3 Modulates AMPK-mTOR Signaling Balance in Cancer Cell Response to DNA Damage

“…In line with this view, here we showed that SMYD3 is part of a multiprotein complex comprising AMPK and mTOR in cancer cells exposed to DNA-damaging drugs. Of note, we also found that SMYD3 methylates AMPK in vitro at Lys411 and Lys424, which belong to an evolutionarily conserved region and are thus likely to be involved in an essential function or mediate an important structural characteristic [38]. As such, it can be speculated that this SMYD3-mediated AMPK post-translational modification may be required for the modulation of the activity of the multiprotein complex comprising SMYD3, AMPK, and mTOR and therefore play a role in the regulation of AMPK-mTOR signaling balance.…”

In-silico analysis of recombinant protein vaccines based on the spike protein of Indonesian SARS-CoV-2 through a reverse vaccinology approach

“… 19 This conserved area has been predicted not to undergo mutation, and to be responsible for a particular function or provide a necessary structural characteristic. 20 Determination of conserved regions aims to ensure that the epitopes used as candidate vaccines show promise for efficacy and coverage of groups or clusters. 21 …”

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“…The abundance of mapped reads at each transcript is then calculated. Recently developed packages have implemented quasi-alignment, which is a fast and efficient alternative to previous techniques and removes the need for genome alignment [139]. Quasi-alignment compares reads to a genome index, and the abundance of each transcript is estimated based upon the number of overlapping reads [139].…”

“…Recently developed packages have implemented quasi-alignment, which is a fast and efficient alternative to previous techniques and removes the need for genome alignment [139]. Quasi-alignment compares reads to a genome index, and the abundance of each transcript is estimated based upon the number of overlapping reads [139]. Subsequent DET analysis then compares the normalised counts of each transcript between study groups to determine the statistical significance of differential expression [114].…”