PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.14.383240v1?rss=1

Authors: Selechnik, D., Richardson, M. F., Hess, M., Hess, A. S., Dodds, K. G., Martin, M., Chan, T. C., Cardilini, A. P. A., Sherman, C., Shine, R., Rollins, L. A.

Abstract:

As technological advancements enhance our ability to study population genetics, we must understand how the intrinsic properties of our datasets influence the decisions we make when designing experiments. Filtering parameter thresholds, such as call rate and minimum minor allele frequency (MAF), are known to affect inferences of population structure in reduced representation sequencing (RRS) studies. However, it is unclear to what extent the impacts of these parameter choices vary across datasets. Here, we reviewed literature on filtering choices and levels of genetic differentiation across RRS studies on wild populations to highlight the diverse approaches that have been used. Next, we hypothesized that choices in filtering thresholds would have the greatest impact when analyzing datasets with low levels of genetic differentiation between populations. To test this hypothesis, we produced seven simulated RRS datasets with varying levels of population structure, and analyzed them using four different combinations of call rate and MAF. We performed the same analysis on two empirical RRS datasets (low or high population structure). Our simulated and empirical results suggest that the effects of filtering choices indeed vary based on inherent levels of differentiation: specifically, choosing stringent filtering choices was important to detect distinct populations that were slightly differentiated, but not those that were highly differentiated. As a result, experimental design and analysis choices need to consider attributes of each specific dataset. Based on our literature review and analyses, we recommend testing a range of filtering parameter choices, and presenting all results with clear justification for ultimate filtering decisions used in downstream analyses.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.13.381442v1?rss=1

Authors: Nagy, A., Munkacsy, G., Gyorffy, B.

Abstract:

Cancer hallmark genes are responsible for the most essential phenotypic characteristics of malignant transformation and progression. In this study, our aim was to estimate the prognostic effect of the established cancer hallmark genes in multiple distinct cancer types. RNA-seq HTSeq counts and survival data from 26 different tumor types were acquired from the TCGA repository. DESeq was used for normalization. Correlations between gene expression and survival were computed using the Cox proportional hazards regression and by plotting Kaplan-Meier survival plots. The false discovery rate was calculated to correct for multiple hypothesis testing. Signatures based on genes involved in genome instability and invasion reached significance in most individual cancer types. Thyroid and glioblastoma were independent of hallmark genes (61 and 54 genes significant, respectively), while renal clear cell cancer and low grade gliomas harbored the most prognostic changes (403 and 419 genes significant, respectively). The eight genes with the highest significance included BRCA1 (genome instability, HR=4.26, p<1E-16), RUNX1 (sustaining proliferative signaling, HR=2.96, p=3.1E-10) and SERPINE1 (inducing angiogenesis, HR=3.36, p=1.5E-12) in low grade glioma, CDK1 (cell death resistance, HR=5.67, p=2.1E-10) in kidney papillary carcinoma, E2F1 (tumor suppressor, HR=0.38, p=2.4E-05) and EREG (enabling replicative immortality, HR=3.23, p=2.1E-07) in cervical cancer, FBP1 (deregulation of cellular energetics, HR=0.45, p=2.8E-07) in kidney renal clear cell carcinoma and MYC (invasion and metastasis, HR=1.81, p=5.8E-05) in bladder cancer. We observed unexpected heterogeneity and tissue specificity when correlating cancer hallmark genes and survival. These results will help to prioritize future targeted therapy development in different types of solid tumors.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.13.380741v1?rss=1

Authors: Olson, N. D., Wagner, J., McDaniel, J., Stephens, S. H., Westreich, S. T., Prasanna, A. G., Johanson, E., Boja, E., Maier, E. J., Serang, O., Jaspez, D., Lorenzo-Salazar, J. M., Munoz-Barrera, A., Rubio-Rodriguez, L. A., Flores, C., Kyriakidis, K., Malousi, A., Shafin, K., Pesout, T., Jain, M., Paten, B., Chang, P.-C., Kolesnikov, A., Nattestad, M., Baid, G., Goel, S., Yang, H., Carroll, A., Eveleigh, R., Bourgey, M., Bourque, G., Li, G., MA, C., Tang, L., DU, Y., Zhang, S., Morata, J., Tonda, R., Parra, G., Trotta, J.-R., Brueffer, C., Demirkaya-Budak, S., Kabakci-Zorlu, D., Turgut, D., Kalay

Abstract:

The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.14.382820v1?rss=1

Authors: Zea, D. J., Laskina, S., Richard, H., Laine, E.

Abstract:

Understanding how protein function has evolved and diversified is of great importance for human genetics and medicine. Here, we tackle the problem of describing the whole transcript variability observed in several species by generalising the definition of splicing graph. We provide a practical solution to building parsimonious evolutionary splicing graphs where each node is a minimal transcript building block defined across species. We show that this approach is useful to assess transcript and alternative splicing events evolutionary conservation on a curated set of 50 genes with functionally distinct isoforms. By scaling up to the whole human protein-coding genome, we identify a few thousands of genes where alternative splicing modulates the number and composition of pseudo-repeats. We have implemented our approach in ThorAxe, an efficient, versatile, and robust computational tool freely available at https://github.com/PhyloSofS-Team/thoraxe. The results are accessible and can be browsed interactively at http://www.lcqb.upmc.fr/ThorAxe.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.15.383281v1?rss=1

Authors: Oh, S., Park, H., Zhang, X.

Abstract:

Recent advances in single cell transcriptomics have allowed us to examine the identify of each single cell, thus have led to discovery of new cell types and provide a high resolution map of cell type composition in tissues. Technologies which can measure another type of data of a single cell in addition to the gene-expression data provide a more comprehensive picture of a cell, and meanwhile pose challenges for data integration tasks. We consider the spatial location of cells, which is an important feature of cells, combined with the cells' gene-expression profiles, to determine the cell type identity. We aim to jointly classify cells based on their locations relative to other cells in the system as well as their gene expression profiles. We have developed scHybridNMF (single-cell Hybrid Nonnegative Matrix Factorization), which performs cell type identification by incorporating single cell gene expression data with cell location data. We combined two classical methods, nonnegative matrix factorization with a k-means clustering scheme, to respectively represent high-dimensional gene expression data and low-dimensional location data together. Our method incorporates a novel cell location term to the gene expression clustering. We show that scHybridNMF can make use of the location data to improve cell type clustering. In particular, we show that under multiple scenarios, including that when the number of genes profiled is low, and when the location data is noisy, scHybridNMF outperforms the standalone algorithms NMF and k-means, and an existing method HMRF which also uses cell location and gene-expression data for cell type identification.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.13.381384v1?rss=1

Authors: Prost, V., Gazut, S., Brüls, T.

Abstract:

The advent of high-throughput metagenomic sequencing has prompted the development of efficient taxonomic profiling methods allowing to measure the presence, abundance and phylogeny of organisms in a wide range of environmental samples. Multivariate sequence-derived abundance data further has the potential to enable inference of ecological associations between microbial populations, but several technical issues need to be accounted for, like the compositional nature of the data, its extreme sparsity and overdispersion, as well as the frequent need to operate in under-determined regimes. The ecological network reconstruction problem is frequently cast into the paradigm of Gaussian graphical models (GGMs) for which efficient structure inference algorithms are available, like the graphical lasso and neighborhood selection. Unfortunately, GGMs or variants thereof can not properly account for the extremely sparse patterns occurring in real-world metagenomic taxonomic profiles. In particular, structural zeros (as opposed to sampling zeros) corresponding to true absences of biological signals fail to be properly handled by most statistical methods. We present here a zero-inflated log-normal graphical model specifically aimed at handling such "biological" zeros, and demonstrate significant performance gains over state-of-the-art statistical methods for the inference of microbial association networks, with most notable gains obtained when analyzing taxonomic profiles displaying sparsity levels on par with real-world metagenomic datasets.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.13.381210v1?rss=1

Authors: Coronado, M. A., Olivier, D. S., Borsatto, K. C., Amaral, M. S., Arni, R. K., Eberle, R. J.

Abstract:

Mosquito borne viruses and their corresponding human infections have a fast growing impact on worldwide public health systems; in particular, Aedes aegypti and Aedes albopictus are transmitting these viruses. Only female mosquitoes bite and spread diseases. Male development in A. aegypti is initiated by male determining factors. These factors are potential candidates for the implementation of vector control strategies. Where at an early stage in gender development female mosquitoes are converted into harmless males. Among these factors, a novel gene, NIX, has been identified that shares moderate identity with transformer-2, one of the key sex-determination genes in Drosophila melanogaster. Hall et al. 2015 described two RNA recognition motifs (RRMs) in the NIX sequence, but a UniProt database search showed that A. aegypti NIX contains three RNA RRM. A homology model of NIX_RRM-3 was generated to investigate the interaction with RNA using molecular dynamics simulations. The sequence AGACGU was the most interesting result. During the binding process A1, G2 and G5 adopt an unusual syn conformation. The results let assume that the C-terminus of NIX_RRM-3 is involved in the recognition process of the target RNA motif (e.g. AG) and controls the interaction between target RNA and A. aegypti NIX.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.13.382465v1?rss=1

Authors: Knight, P., Gauthier, M.-P. L., Pardo, C. E., Darst, R. P., Riva, A., Kladde, M. P., Bacher, R.

Abstract:

Differential DNA methylation and chromatin accessibility are associated with disease development, particularly cancer. Methods that allow profiling of these epigenetic mechanisms in the same reaction and at the single-molecule or single-cell level continue to emerge. However, a challenge lies in jointly visualizing and analyzing the heterogeneous nature of the data and extracting regulatory insight. Here, we developed methylscaper, a visualization framework for simultaneous analysis of DNA methylation and chromatin landscapes. Methylscaper implements a weighted principle component analysis that orders sequencing reads, each providing a record of the chromatin state of one epiallele, and reveals patterns of nucleosome positioning, transcription factor occupancy, and DNA methylation. We demonstrate methylscaper's utility on a long-read, single-molecule methyltransferase accessibility protocol for individual templates (MAPit) dataset and a single-cell nucleosome, methylation, and transcription sequencing (scNMT-seq) dataset. In comparison to other procedures, methylscaper is able to readily identify chromatin features that are biologically relevant to transcriptional status while scaling to larger datasets. Methylscaper, is available on GitHub at https://github.com/rhondabacher/methylscaper.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.13.382283v1?rss=1

Authors: Kurgan, G., Turk, R., Li, H., Rettig, G. R., Jacobi, A. M., Tso, L., Mertens, M., Noten, R., Florus, K., Behlke, M. A., Wang, Y., McNeill, M. S.

Abstract:

CRISPR systems enable targeted genome editing in a wide variety of organisms by introducing single- or double-strand DNA breaks, which are repaired using endogenous molecular pathways. Characterization of on- and off-target editing events from CRISPR proteins can be evaluated using targeted genome resequencing. We characterized DNA repair footprints that result from non-homologous end joining (NHEJ) after double stranded breaks (DSBs) were introduced by Cas9 or Cas12a for >500 paired treatment/control experiments. We found that building our understanding into a novel analysis tool (CRISPAltRations) improved results quality. We validated our software using simulated rhAmpSeq amplicon sequencing data (11 gRNAs and 603 on- and off-target locations) and demonstrate that CRISPAltRations outperforms other publicly available software tools in accurately annotating CRISPR-associated indels and homology directed repair (HDR) events. We enable non-bioinformaticians to use CRISPAltRations by developing a web-accessible, cloud-hosted deployment, which allows rapid batch processing of samples in a graphical user-interface (GUI) and complies with HIPAA security standards. By ensuring that our software is thoroughly tested, version controlled, and supported with a UI we enable resequencing analysis of CRISPR genome editing experiments to researchers no matter their skill in bioinformatics.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.13.381475v1?rss=1

Authors: Pabis, K.

Abstract:

The "theory of resistant biomolecules" posits that long-lived species show resistance to molecular damage at the level of their biomolecules. Here, we test this hypothesis in the context of mitochondrial DNA (mtDNA) as it implies that predicted mutagenic DNA motifs should be inversely correlated with species maximum lifespan (MLS). First, we confirmed that guanine-quadruplex (GQ) and direct repeat (DR) motifs are mutagenic, as they associate with mtDNA deletions in the human major arc of mtDNA, while also adding mirror repeat (MR) and intramolecular triplex motifs to a growing list of potentially mutagenic features. What is more, triplex motifs showed disease-specific associations with deletions and an apparent interaction with GQ motifs. Surprisingly, even though DR, MR and GQ motifs were associated with mtDNA deletions, their correlation with MLS was explained by the biased base composition of mtDNA. Only triplex motifs negatively correlated with MLS and these results remained stable even after adjusting for body mass, phylogeny, mtDNA base composition and effective number of codons. Taken together, our work highlights the importance of base composition for the comparative biogerontology of mtDNA and suggests that future research on mitochondrial triplex motifs is warranted.

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