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Biology subjects

Sumi, S.

Publications and source records attributed to Sumi, S..

2 recordsLinked to original sources

Alignment-free discovery of conserved RNA structures in the human genome

Many functional RNAs were first found as conserved RNA structures, and their roles in gene regulation were identified later. In humans and other vertebrates, imperfect genome alignments have hindered the discovery of such structures. Here we present RLMHelix, which selects candidate conserved RNA structures from single sequences before any alignment by scoring helices supported by language-model dependencies and thermodynamics. Through distillation, RLMHelix rapidly scans the transcribed human genome and ranks known RNAs higher than conventional thermodynamic scanning. Two-thirds of 15,821 candidates are new to four earlier genome-wide screens, yet agree with in-cell probing, and over half retain structural signal in homologues. In eCLIP data and in exploratory pull-downs, higher RLMHelix scores were associated with more binding by double-stranded RNA-binding proteins. By linking candidates to their binding proteins, RLMHelix yields testable functional hypotheses and illuminates a path from unrecognized human RNA structures to RNA biology and therapeutic targets.

bioinformatics↗

Representation learning applications in biological sequence analysis

Remarkable advances in high-throughput sequencing have resulted in rapid data accumulation, and analyzing biological (DNA/RNA/protein) sequences to discover new insights in biology has become more critical and challenging. To tackle this issue, the application of natural language processing (NLP) to biological sequence analysis has received increased attention, because biological sequences are regarded as sentences and k-mers in these sequences as words. Embedding is an essential step in NLP, which converts words into vectors. This transformation is called representation learning and can be applied to biological sequences. Vectorized biological sequences can be used for function and structure estimation, or as inputs for other probabilistic models. Given the importance and growing trend in the application of representation learning in biology, here, we review the existing knowledge in representation learning for biological sequence analysis.

bioinformatics↗