scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single-cell data.

Summary: scFates provides an extensive toolset for the analysis of dynamic trajectories comprising tree learning, feature association testing, branch differential expression and with a focus on cell biasing and fate splits at the level of bifurcations. It is meant to be fully integrated into the scanpy ecosystem for seamless analysis of trajectories from single-cell data of various modalities (e.g.

Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes.

Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and nongenetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of copy number variations from scRNA-seq.

Cell segmentation in imaging-based spatial transcriptomics.

Single-molecule spatial transcriptomics protocols based on in situ sequencing or multiplexed RNA fluorescent hybridization can reveal detailed tissue organization. However, distinguishing the boundaries of individual cells in such data is challenging and can hamper downstream analysis. Current methods generally approximate cells positions using nuclei stains.

RNA velocity-current challenges and future perspectives.

RNA velocity has enabled the recovery of directed dynamic information from single-cell transcriptomics by connecting measurements to the underlying kinetics of gene expression. This approach has opened up new ways of studying cellular dynamics. Here, we review the current state of RNA velocity modeling approaches, discuss various examples illustrating limitations and potential pitfalls, and provide guidance on how the ensuing challenges may be addressed.

Dental cell type atlas reveals stem and differentiated cell types in mouse and human teeth.

Understanding cell types and mechanisms of dental growth is essential for reconstruction and engineering of teeth. Therefore, we investigated cellular composition of growing and non-growing mouse and human teeth. As a result, we report an unappreciated cellular complexity of the continuously-growing mouse incisor, which suggests a coherent model of cell dynamics enabling unarrested growth.

Spatiotemporal structure of cell fate decisions in murine neural crest.

Neural crest cells are embryonic progenitors that generate numerous cell types in vertebrates. With single-cell analysis, we show that mouse trunk neural crest cells become biased toward neuronal lineages when they delaminate from the neural tube, whereas cranial neural crest cells acquire ectomesenchyme potential dependent on activation of the transcription factor The choices that neural crest cells make to become sensory, glial, autonomic, or mesenchymal cells can be formalized as a series of sequential binary decisions. Each branch of the decision tree involves initial coactivation of bipotential properties followed by gradual shifts toward commitment.

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 throughput. 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.

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.

Evolution of Local Mutation Rate and Its Determinants.

Unlabelled: 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.

APOBEC-induced mutations in human cancers are strongly enriched on the lagging DNA strand during replication.

APOBEC3A and APOBEC3B, cytidine deaminases of the APOBEC family, are among the main factors causing mutations in human cancers. APOBEC deaminates cytosines in single-stranded DNA (ssDNA). A fraction of the APOBEC-induced mutations occur as clusters ("kataegis") in single-stranded DNA produced during repair of double-stranded breaks (DSBs).

Probing-directed identification of novel structured RNAs.

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.

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.

[Genome-wide identification of functional noncoding RNAs,].

Non-coding RNAs (ncRNAs) are functional transcripts that do not encode proteins. They are involved in many regulation pathways. The only general characteristic shared by many (but not all) known RNAs is folding into complex shapes that are crucial to function and thus should be conserved.

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.

[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.