Ruslan A. Soldatov

RNA velocity of single cells

RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena such as embryogenesis or tissue regeneration. Here we show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.RNA velocity, estimated in single cells by comparison of spliced and unspliced mRNA, is a good indicator of transcriptome dynamics and will provide a useful tool for analysis of developmental lineage.

RNASurface: fast and accurate detection of locally optimal potentially structured RNA segments

MOTIVATION During the past decade, new classes of non-coding RNAs (ncRNAs) and their unexpected functions were discovered. Stable secondary structure is the key feature of many non-coding RNAs. Taking into account huge amounts of genomic data, development of computational methods to survey genomes for structured RNAs remains an actual problem, especially when homologous sequences are not available for comparative analysis. Existing programs scan genomes with a fixed window by efficiently constructing a matrix of RNA minimum free energies. A wide range of lengths of structured RNAs necessitates the use of many different window lengths that substantially increases the output size and computational efforts. RESULTS In this article, we present an algorithm RNASurface to efficiently scan genomes by constructing a matrix of significance of RNA secondary structures and to identify all locally optimal structured RNA segments up to a predefined size. RNASurface significantly improves precision of identification of known ncRNA in Bacillus subtilis. AVAILABILITY AND IMPLEMENTATION RNASurface C source code is available from http://bioinf.fbb.msu.ru/RNASurface/downloads.html.

Polymerase ζ Activity Is Linked to Replication Timing in Humans: Evidence from Mutational Signatures

Replication timing is an important determinant of germline mutation patterns, with a higher rate of point mutations in late replicating regions. Mechanisms underlying this association remain elusive. One of the suggested explanations is the activity of error-prone DNA polymerases in late-replicating regions. Polymerase zeta (pol ζ), an essential error-prone polymerase biased toward transversions, also has a tendency to produce dinucleotide mutations (DNMs), complex mutational events that simultaneously affect two adjacent nucleotides. Experimental studies have shown that pol ζ is strongly biased toward GC→AA/TT DNMs. Using primate divergence data, we show that the GC→AA/TT pol ζ mutational signature is the most frequent among DNMs, and its rate exceeds the mean rate of other DNM types by a factor of approximately 10. Unlike the overall rate of DNMs, the pol ζ signature drastically increases with the replication time in the human genome. Finally, the pol ζ signature is enriched in transcribed regions, and there is a strong prevalence of GC→TT over GC→AA DNMs on the nontemplate strand, indicating association with transcription. A recurrently occurring GC→TT DNM in HRAS and SOD1 genes causes the Costello syndrome and amyotrophic lateral sclerosis correspondently; we observe an approximately 1 kb long mutation hotspot enriched by transversions near these DNMs in both cases, suggesting a link between these diseases and pol ζ activity. This study uncovers the genomic preferences of pol ζ, shedding light on a novel cause of mutational heterogeneity along the genome.

Evolution of Local Mutation Rate and Its Determinants

Mutation rate varies along the human genome, and part of this variation is explainable by measurable local properties of the DNA molecule. Moreover, mutation rates differ between orthologous genomic regions of different species, but the drivers of this change are unclear. Here, we use data on human divergence from chimpanzee, human rare polymorphism, and human de novo mutations to predict the substitution rate at orthologous regions of non-human mammals. We show that the local mutation rates are very similar between human and apes, implying that their variation has a strong underlying cryptic component not explainable by the known genomic features. Mutation rates become progressively less similar in more distant species, and these changes are partially explainable by changes in the local genomic features of orthologous regions, most importantly, in the recombination rate. However, they are much more rapid, implying that the cryptic component underlying the mutation rate is more ephemeral than the known genomic features. These findings shed light on the determinants of mutation rate evolution. Key words: local mutation rate, molecular evolution, recombination rate.

Probing-directed identification of novel structured RNAs

ABSTRACT Transcripts often harbor RNA elements, which regulate cell processes co- or post-transcriptionally. The functions of many regulatory RNA elements depend on their structure, thus it is important to determine the structure as well as to scan genomes for structured elements. State of the art ab initio approaches to predict structured RNAs rely on DNA sequence analysis. They use 2 major types of information inferred from a sequence: thermodynamic stability of an RNA structure and evolutionary footprints of base-pair interactions. In recent years, chemical probing of RNA has arisen as an alternative source of structural information. RNA probing experiments detect positions accessible to specific types of chemicals or enzymes indicating their propensity to be in a paired or unpaired state. There exist several strategies to integrate probing data into RNA secondary structure prediction algorithms that substantially improve the prediction quality. However, whether and how probing data could contribute to detection of structured RNAs remains an open question. We previously developed the energy-based approach RNASurface to detect locally optimal structured RNA elements. Here, we integrate probing data into the RNASurface energy model using a general framework. We show that the use of experimental data allows for better discrimination of ncRNAs from other transcripts. Application of RNASurface to genome-wide analysis of the human transcriptome with PARS data identifies previously undetectable segments, with evidence of functionality for some of them.

Genome-wide search for functional noncoding RNA

Noncoding RNAs (ncRNAs) are functional transcripts that do not encode proteins; they are involved in many regulation pathways. The only common feature shared by most (but not all) known ncRNAs is their ability to fold into secondary structures that are crucial for their functioning and thus should be conserved. This fact can be used for genome-wide prediction of ncRNAs. The approach employed in this study was based on computing local base pairing probabilities and further maximizing the total pairing probability for a segment using the Nussinoff-like algorithm. The suggested method was shown to efficiently predict known ncRNAs and possibly some new ncRNAs.

Differential processing of small RNAs during endoplasmic reticulum stress

The accumulation of misfolded proteins in the endoplasmic reticulum (ER) lumen due to the disruption of the homeostatic system of the ER leads to the induction of the ER stress response. Cellular stress-induced pathways globally transform genes expression on both the transcriptional and post-transcriptional levels with small RNA involvement as regulators of the stress response. The modulation of small RNA processing might represent an additional layer of a complex stress response program. However, it is poorly understood. Here, we studied changes in expression and small RNAs processing upon ER stress in Jurkat T-cells. Induced by ER-stress, depletion of miRNAs among small RNA composition was accompanied by a global decrease of 3′ mono-adenylated, mono-cytodinylated and a global increase of 3′ mono-uridinylated miRNA isoforms. We observed the specific subset of differentially expressed microRNAs, and also the dramatic induction of 32-nt tRNA fragments precisely phased to 5′ and 3′ ends of tRNA from a subset of tRNA isotypes. The induction of these tRNA fragments was linked to Angiogenin RNase, which mediates translation inhibition. Overall, the global perturbations of the expression and processing of miRNAs and tiRNAs were the most prominent features of small RNA transcriptome changes upon ER stress.

Statistical methods of comparative genomic analysis based on diffusion processes

Comparative genomics is a powerful tool of genome functional specificity predictions and investigation of evolution specificity. Background of a large field of bioinformatics investigations is a computation of different scores of sequences and comparing them with a threshold. Comparative genomic analysis involves scores comparing for orthological groups of genetic objects. In this paper we represent a statistical approach to comparative genomic analysis, that based on investigation of diffusion in sequence space determined by neutral evolution of sequences. Using this approach we represent several statistics for selection pressure estimation and analyze statistics for several biological problems. We formulate technology of statistics applying to obtain new biological information. This approach is represented as Java-class library.