PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.23.352179v1?rss=1

Authors: Yones, C. A., Raad Macchiaroli, J., Bugnon, L., Milone, D. H., Stegmayer, G.

Abstract:

Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that have a key role in the regulation of gene expression. The importance of miRNAs is widely acknowledged by the community nowadays, and the precise prediction of novel candidates with computational methods is still very needed. This could be done by searching homologous with sequence alignment tools, but this will be restricted only to sequences very similar to the known miRNA precursors (pre-miRNAs). Furthermore, other important properties of pre-miRNAs, such as the secondary structure, are not taken into account by these methods. Many machine learning approaches were proposed in the last years to fill this gap, but these methods were tested in very controlled conditions, which are not fulfilled, for example, when predicting in newly sequenced genomes, where no miRNAs are known. If these methods are used under real conditions, the precision achieved is far from the one published. Results: This work provides a novel approach for dealing with the computational prediction of pre-miRNAs: a convolutional deep residual neural network. The proposed model has been tested on several complete genomes of animals and plants, achieving a precision up to 5 times higher than other approaches at the same recall rates. Also, a novel validation methodology is used to ensure that the performance reported can be achieved when using the method on new unknown species. Availability: To provide fast an easy access to mirDNN, a web demo is available in http://sinc.unl.edu.ar/web-demo/mirdnn/. It can process fasta files with multiple sequences to calculate the prediction scores, and can generate the nucleotide importance plots. The full source code of this project is available at http://sourceforge.net/projects/sourcesinc/files/mirdnn Contact: [email protected]

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.24.353425v1?rss=1

Authors: Kanyerezi, S., Nabisubi, P.

Abstract:

Introduction: Tuberculosis (TB) is the leading cause of morbidity and mortality globally, responsible for an estimated annual 10.0 million new cases and 1.3 million deaths among infectious diseases with Africa contributing a quarter of these cases in 2019. Classification of Mycobacterium tuberculosis (MTB) strains is important in understanding their geographical predominance and pathogenicity. Different studies have gone ahead to classify MTB using different methods. Some of these include; RFLP, spoligotyping, MIRU-VNTR and SNP set based phylogeny. The SNP set based classification has been found to be in concordance with the region of difference (RD) analysis of MTB complex classification system. In Uganda, the most common cause of pulmonary tuberculosis (PTB) is Uganda genotype of MTB and accounts for up to 70 % of isolates. Methods: Sequenced MTB genome samples were retrieved from NCBI and others from local sequencing projects. The genomes were subjected to snippy (a rapid haploid variant calling and core genome alignment) to call variants and annotate them. Outputs from snippy were used to classify the isolates into Uganda genotypes and Non Ugandan genotypes based on 62 SNP set. The Ugandan genotype isolates were later subjected to 413 SNP set and then to a pan genome wide association analysis. Results: 6 Uganda genotype isolates were found not to classify as either Uganda I or II genotypes based on the 62 SNP set. Using the 413 SNP set, the 6 Uganda genotype isolates were found to have only one SNP out of the 7 SNPs that classify the Uganda I genotypes. They were also found to have both missense and frameshift mutations within the ctpH gene whereas the rest of Uganda I that had a mutation within this gene, was a missense. Conclusion: Among the Uganda genotypes genomes, Uganda I genomes are unstable. We used publicly available datasets to perform analysis like mapping, variant calling, mixed infection, pan-genome analysis to investigate and compare evolution of the Ugandan genotype.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.22.351049v1?rss=1

Authors: Qin, T., Lee, C., Cavalcante, R., Orchard, P., Yao, H., Zhang, H., Wang, S., Patil, S., Boyle, A. P., Sartor, M. A.

Abstract:

Revealing the gene targets of distal regulatory elements is challenging yet critical for interpreting regulome data. Experiment-derived enhancer-gene links are restricted to a small set of enhancers and/or cell types, while the accuracy of genome-wide approaches remains elusive due to the lack of a systematic evaluation. We combined multiple spatial and in silico approaches for defining enhancer locations and linking them to their target genes aggregated across >500 cell types, generating 1,860 human genome-wide distal Enhancer to Target gene Definitions (EnTDefs). To evaluate performance, we used gene set enrichment testing on 87 independent ENCODE ChIP-seq datasets of 34 transcription factors (TFs) and assessed concordance of results with known TF Gene Ontology (GO) annotations., assuming that greater concordance with TF-GO annotation signifies better enrichment results and thus more accurate enhancer-to-gene assignments. Notably, the top ranked 741 (40%) EnTDefs significantly outperformed the common, naïve approach of linking distal regions to the nearest genes (FDR < 0.05), and the top 10 ranked EnTDefs performed well when applied to ChIP-seq data of other cell types. These general EnTDefs also showed comparable performance to EnTDefs generated using cell-type-specific data. Our findings illustrate the power of our approach to provide genome-wide interpretation regardless of cell type.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.22.349910v1?rss=1

Authors: Ignatov, D. I., Khvorykh, G. V., Khrunin, A. V., Nikolic, S., Shaban, M., Petrova, E. A., Koltsova, E. A., Takelait, F., Egurnov, D.

Abstract:

Missing genotypes can affect the efficacy of machine learning approaches to identify the risk genetic variants of common diseases and traits. The problem occurs when genotypic data are collected from different experiments with different DNA microarrays, each being characterised by its pattern of uncalled (missing) genotypes. This can prevent the machine learning classifier from assigning the classes correctly. To tackle this issue, we used well-developed notions of object-attribute biclusters and formal concepts that correspond to dense subrelations in the binary relation patients x SNPs. The paper contains experimental results on applying a biclustering algorithm to a large real-world dataset collected for studying the genetic bases of ischemic stroke. The algorithm could identify large dense biclusters in the genotypic matrix for further processing, which in return significantly improved the quality of machine learning classifiers. The proposed algorithm was also able to generate biclusters for the whole dataset without size constraints in comparison to the In-Close4 algorithm for generation of formal concepts.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.22.350009v1?rss=1

Authors: Moller, P. L., Holley, G., Beyter, D., Nyegaard, M., Halldorsson, B.

Abstract:

Background: The development of long read sequencing (LRS) has led to greater access to the human genome. LRS produces long read lengths at the cost of high error rates and has shown to be more useful in calling structural variants than short read sequencing (SRS) data. In this paper we evaluate how to use LRS data from Oxford Nanopore Technologies (ONT) to call small variants in regions in- and outside the reach of SRS. Results: Calling single nucleotide polymorphisms (SNPs) with ONT data has comparable accuracy to Illumina when evaluating against the Genome in a Bottle truth set v4.2. In the major histocompatibility complex (MHC) and regions where mapping short reads is difficult, the F-measure of ONT calls exceeds those of short reads by 2-4% when sequence coverage is 20X or greater. We develop recommendations for how to perform small variant calling with LRS data and improve current approaches to the difficult regions by re-genotyping variants to increase the F-measure from 97.24% to 98.78%. Furthermore, we show how LRS can call variants in genomic regions inaccessible to SRS, including medically relevant genes such as STRC and CFC1B. Conclusions: Although small variant calling in LRS data is still immature, current methods are clearly useful in difficult and inaccessible regions of the genome, enabling variant calling in medically relevant genes not accessible to SRS.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.22.350611v1?rss=1

Authors: Baccini, F., Bianchini, M., Geraci, F.

Abstract:

In this paper, we show that quantifying histone modifications by counting the number of high-resolution peaks in each gene allows to build profiles of these epigenetic marks, associating them to a phenotype. The significance of this approach is verified by applying graph-cut techniques for assessing the differentiation between myeloid and lymphoid cells in haematopoiesis, i.e. the process through which all the different types of blood cells originate starting from a unique cell type. The experiments are conducted on a population of samples from 24 cell types involved in haematopoiesis. Six profiles are constructed for each cell type, based on a different histone modification signal. Following the experimentally verified idea that the peak number distribution per gene behaves similarly to gene expression, the profile computation employs standard differential analysis tools to find genes whose epigenetic modifications are related to a given phenotype. Next, six similarity networks of cell types are constructed, based on each histone modification, and then combined into a unique one through similarity network fusion. Finally, the similarity networks are transformed into dissimilarity graphs, to which two different cuts are applied and compared to evaluate the classic differentiation between myeloid and lymphoid cells. The results show that all histone modifications contribute almost equally to the myeloid/lymphoid differentiation, and this is also confirmed by the analysis of the fused network. However, they also suggest that histone modifications may not be the only mechanism for regulating the differentiation of hematopoietic cells.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.20.347500v1?rss=1

Authors: Nohr, A. K., Hanghoj, K., Erill, G. G., Moltke, I., Albrechtsen, A.

Abstract:

Estimation of relatedness between pairs of individuals is important in many genetic research areas. When estimating relatedness, it is important to account for admixture if this is present. However, the methods that can account for admixture are all based on genotype data as input, which is a problem for low depth next-generation sequencing (NGS) data from which genotypes are called with high uncertainty. Here we present a software tool, NGSremix, for maximum likelihood estimation of relatedness between pairs of admixed individuals from low depth NGS data, which takes the uncertainty of the genotypes into account via genotype likelihoods. Using both simulated and real NGS data for admixed individuals with an average depth of 4x or below we show that our method works well and clearly outperforms all the commonly used state-of-the-art relatedness estimation methods PLINK, KING, relateAdmix, and ngsRelate that all perform quite poorly. Hence, NGSremix is a useful new tool for estimating relatedness in admixed populations from low-depth NGS data. NGSremix is implemented in C/C++ in a multi-threaded software and is freely available on Github https://github.com/KHanghoj/NGSremix .

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.23.352666v1?rss=1

Authors: Le, B. L., Andreoletti, G., Oskotsky, T., Vallejo-Gracia, A., Rosales, R., Yu, K. Z., Kosti, I., Leon, K. E., Bunis, D. G., Li, C., Kumar, G. R., White, K. M., Garcia-Sastre, A. E., Ott, M., Sirota, M.

Abstract:

The novel SARS-CoV-2 virus emerged in December 2019 and has few effective treatments. We applied a computational drug repositioning pipeline to SARS-CoV-2 differential gene expression signatures derived from publicly available data. We utilized three independent published studies to acquire or generate lists of differentially expressed genes between control and SARS-CoV-2-infected samples. Using a rank-based pattern matching strategy based on the Kolmogorov-Smirnov Statistic, the signatures were queried against drug profiles from Connectivity Map (CMap). We validated sixteen of our top predicted hits in live SARS-CoV-2 antiviral assays in either Calu-3 or 293T-ACE2 cells. Validation experiments in human cell lines showed that 11 of the 16 compounds tested to date (including clofazimine, haloperidol and others) had measurable antiviral activity against SARS-CoV-2. These initial results are encouraging as we continue to work towards a further analysis of these predicted drugs as potential therapeutics for the treatment of COVID-19.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.22.351221v1?rss=1

Authors: Schilder, B. M., Humphrey, J., Raj, T.

Abstract:

echolocatoR integrates a diverse suite of statistical and functional fine-mapping tools in order to identify, test enrichment in, and visualize high-confidence causal consensus variants in any phenotype. It requires minimal input from users (a summary statistics file), can be run in a single R function, and provides extensive access to relevant datasets (e.g. reference linkage disequilibrium (LD) panels, quantitative trait loci (QTL) datasets, genome-wide annotations, cell type-specific epigenomics, thereby enabling rapid, robust and scalable end-to-end fine-mapping investigations.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.23.348706v1?rss=1

Authors: Topin, J., Bouysset, C., Kim, Y., Rhyu, M., Fiorucci, S., Golebiowski, J.

Abstract:

Bitter taste receptors (TAS2Rs) are a poorly understood subgroup of G protein-coupled receptors (GPCR). No experimental structure of these receptors is available and key-residues controlling their function remain mostly unknown. Here, we have identified the functional microswitches that encode agonist sensing and downstream signaling mechanisms within TAS2Rs sequences. We thoroughly re-aligned the amino-acid sequences of the 25 human TAS2Rs considering residue conservations and all the experimental data from the literature as constraints. As a test case, an accurate homology model of TAS2R16 was constructed and examined by site-directed mutagenesis and in vitro functional assays. Conserved motifs acting as microswitches during agonist-sensing and receptor activation were pinpointed by comparison with the current knowledge on class A GPCRs. Unravelling these sequence-function relationships is of utmost importance to streamline how TAS2Rs functions are encrypted in their sequence.

Copy rights belong to original authors. Visit the link for more info