Analysis of single-cell RNA-seq data begins with the pre-processing of reads to generate count matrices. We investigate algorithm choices for the challenges of pre-processing, and describe a workflow that balances efficiency and accuracy. Our workflow is based on the kallisto and bustools programs, and is near-optimal in speed and memory. The workflow is modular, and we demonstrate its flexibility by showing how it can be used for RNA velocity analyses.

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The mechanisms regulating the generation of cell diversity in the mammalian cerebral cortex are beginning to be elucidated. In that regard, Hairy/Enhancer of split (Hes) 1 and 5 are basic helix-loop-helix (bHLH) factors that inhibit the differentiation of pluripotent cortical progenitors into neurons. In contrast, a related Hes family member termed Hes6 promotes neurogenesis. It is shown here that knockdown of endogenous Hes6 causes supernumerary cortical progenitors to differentiate into cells that exhibit an astrocytic morphology and express the astrocyte marker protein GFAP. Conversely, exogenous Hes6 expression in cortical progenitors inhibits astrocyte differentiation. The negative effect of Hes6 on astrocyte differentiation is independent of its ability to promote neuronal differentiation. We also show that neither its proneuronal nor its anti-gliogenic functions appear to depend on Hes6 ability to bind to DNA via the basic arm of its bHLH domain. Both of these activities require Hes6 to be localized to nuclei, but only its anti-gliogenic function depends on two short peptides, LNHLL and WRPW, that are conserved in all Hes6 proteins. These findings suggest that Hes6 is an important regulator of the neurogenic phase of cortical development by promoting the neuronal fate while suppressing astrocyte differentiation. They suggest further that separate molecular mechanisms underlie the proneuronal and anti-gliogenic activities of Hes6 in cortical progenitor cells.

Motivation:Genome alignment of reads is the first step of most genome analysis workflows. In the case of RNA-Seq, transcriptome pseudoalignment of reads is a fast alternative to genome alignment, but the different "coordinate systems" of the genome and transcriptome have made it difficult to perform direct comparisons between the approaches. Results:We have developed tools for converting genome alignments to transcriptome pseudoalignments, and conversely, for projecting transcriptome pseudoalignments to genome alignments. Using these tools, we performed a direct comparison of genome alignment with transcriptome pseudoalignment. We find that both approaches produce similar quantifications. This means that for many applications genome alignment and transcriptome pseudoalignment are interchangeable.Availability and Implementation: bam2tcc is a C++14 software for converting alignments in SAM/BAM format to transcript compatibility counts (TCCs) and is available at https://github.com/pachterlab/bam2tcc. kallisto genomebam is a user option of kallisto that outputs a sorted BAM file in genome coordinates as part of transcriptome pseudoalignment. The feature has been released with kallisto v0.44.0, and is available at https://pachterlab.github.io/kallisto/.

Purpose Symptoms associated with COVID-19 infection have made the assessment and triage of cancer patients extremely complicated. The purpose of this paper is to describe the development and implementation of a COVID-19 screening tool for oncology telephone triage. Methods An Ambulatory Oncology Clinical Nurse Educator and three faculty members worked on the development of an oncology specific triage tool based on the challenges that oncology nurses were having with the generic COVID triage tool. A thorough search of the published literature, as well as pertinent websites, verified that no screening tool for oncology patients was available. Results The screening tool met a number of essential criteria: (1) simple and easy to use, (2) included the most common signs and symptoms as knowledge of COVID-19 infection changed, (3) was congruent with the overall screening procedures of the medical center, (4) included questions about risk factors for and environmental exposures related to COVID-19, and (5) assessed patient’s current cancer history and treatment status. Over a period of 3 weeks, the content and specific questions on the tool were modified based on information obtained from a variety of sources and feedback from the triage nurses. Conclusion Within 1 month, the tool was developed and implemented in clinical practice. Oncology clinicians can modify this tool to triage patients as well as to screen patients in a variety of outpatient settings (e.g., chemotherapy infusion units, radiation therapy departments). The tool will require updates and modifications based on available resources and individual health care organizations’ policies and procedures.