Identifying cell state-associated alternative splicing events and their coregulation.

Alternative splicing shapes the transcriptome and contributes to each cell's unique identity, but single-cell RNA sequencing (scRNA-seq) has struggled to capture the impact of alternative splicing. We previously showed that low recovery of mRNAs from single cells led to erroneous conclusions about the cell-to-cell variability of alternative splicing. Here, we present a method, Psix, to confidently identify splicing that changes across a landscape of single cells, using a probabilistic model that is robust against the data limitations of scRNA-seq. Its autocorrelation-inspired approach finds patterns of alternative splicing that correspond to patterns of cell identity, such as cell type or developmental stage, without the need for explicit cell clustering, labeling, or trajectory inference. Applying Psix to data that follow the trajectory of mouse brain development, we identify exons whose alternative splicing patterns cluster into modules of coregulation. We show that the exons in these modules are enriched for binding by distinct neuronal splicing factors and that their changes in splicing correspond to changes in expression of these splicing factors. Thus, Psix reveals cell type-dependent splicing patterns and the wiring of the splicing regulatory networks that control them. Our new method will enable scRNA-seq analysis to go beyond transcription to understand the roles of post-transcriptional regulation in determining cell identity.

New substitutions on NS1 protein from influenza A (H1N1) virus: Bioinformatics analyses of Indian strains isolated from 2009 to 2020.

Background and Aims: Nonstructural (NS1) protein is mainly involved in virulence and replication of several viruses, including influenza virus A (H1N1); surveillance of the latter started in India in 2009. The objective of this study was to identify the new substitutions in NS1 protein from the influenza virus A (H1N1) pandemic 2009 (pdm09) strain isolated in India. Methods: The sequences of NS1 proteins from influenza A(H1N1) pdm09 strains isolated in India were obtained from publicly available databases. Multiple sequence alignment and phylogeny analyses were performed to confirm the "consistent substitutions" on NS1 protein from H1N1 (pdm09) Indian strains. Here, "consistent substitutions" were defined as the substitutions observed in all the sequences isolated in a year. Comparative analyses were performed among NS1 Indian sequences from A(H1N1) pdm09, A (H1N1) seasonal and A(H3N2) strains, and from A (H1N1) pdm09 global strains. Results: Eight substitutions were identified in the NS1 Indian sequence from the A(H1N1) pdm09 strain, two in RBD, five in ED, and one in the linker region. Three new substitutions were reported in this study at NS1 sequence positions 2, 80, and 155, which evolved within 2015-2019 and became "consistent." These new substitutions were associated with conservative paired substitutions in the alternative domains of the NS1 protein. Three paired substitutions were (i) D2E and E125D, (ii) T80A and A155T, and (iii) E55K and K131E. Conclusions: This study indicates the continuous evolution of NS1 protein from the influenza A virus. The new substitutions at positions 2 and 80 occurred in the RNA binding and eIF4GI binding domains. The D2E substitution evolved simultaneously with the E125D substitution that involved viral replication. The third new substitution at position 155 occurred in the PI3K binding domain. The possible consequences of these substitutions on host-pathogen interactions are subject to further experimental and computational verification.

Temperature and Latitude Correlate with SARS-CoV-2 Epidemiological Variables but not with Genomic Change Worldwide.

The SARS-CoV-2 virus that causes the COVID-19 disease has spread quickly and massively around the entire globe, causing millions of confirmed cases and deaths worldwide. The disease poses a serious ongoing threat to public health. This study aims to understand the disease potential of the virus in different regions by studying how average spring temperature and its strong predictor, latitude, affect epidemiological variables such as disease incidence, mortality, recovery cases, active cases, testing rate, and hospitalization. We also seek to understand the association of temperature and geographic coordinates with viral genomics. Epidemiological data along with temperature, latitude, longitude, and preparedness index were collected for different countries and US states during the early stages of the pandemic. Our worldwide epidemiological analysis showed a significant correlation between temperature and incidence, mortality, recovery cases and active cases. The same tendency was found with latitude, but not with longitude. In the US, we observed no correlation between temperature or latitude and epidemiological variables. Interestingly, longitude was correlated with incidence, mortality, active cases, and hospitalization. An analysis of mutational change and mutational change per time in 55 453 aligned SARS-CoV-2 genome sequences revealed these parameters were uncorrelated with temperature and geographic coordinates. The epidemiological trends we observed worldwide suggest a seasonal effect for the disease that is not directly controlled by the genomic makeup of the virus. Future studies will need to determine if correlations are more likely the result of effects associated with the environment or the innate immunity of the host.

Design considerations for workflow management systems use in production genomics research and the clinic.

The changing landscape of genomics research and clinical practice has created a need for computational pipelines capable of efficiently orchestrating complex analysis stages while handling large volumes of data across heterogeneous computational environments. Workflow Management Systems (WfMSs) are the software components employed to fill this gap. This work provides an approach and systematic evaluation of key features of popular bioinformatics WfMSs in use today: Nextflow, CWL, and WDL and some of their executors, along with Swift/T, a workflow manager commonly used in high-scale physics applications. We employed two use cases: a variant-calling genomic pipeline and a scalability-testing framework, where both were run locally, on an HPC cluster, and in the cloud. This allowed for evaluation of those four WfMSs in terms of language expressiveness, modularity, scalability, robustness, reproducibility, interoperability, ease of development, along with adoption and usage in research labs and healthcare settings. This article is trying to answer, which WfMS should be chosen for a given bioinformatics application regardless of analysis type?. The choice of a given WfMS is a function of both its intrinsic language and engine features. Within bioinformatics, where analysts are a mix of dry and wet lab scientists, the choice is also governed by collaborations and adoption within large consortia and technical support provided by the WfMS team/community. As the community and its needs continue to evolve along with computational infrastructure, WfMSs will also evolve, especially those with permissive licenses that allow commercial use. In much the same way as the dataflow paradigm and containerization are now well understood to be very useful in bioinformatics applications, we will continue to see innovations of tools and utilities for other purposes, like big data technologies, interoperability, and provenance.