Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

Integrative Genomics Viewer

Hi-C experiments explore the 3D structure of the genome, generating terabases of data to create high-resolution contact maps. Here, we introduce Juicer, an open-source tool for analyzing terabase-scale Hi-C datasets. Juicer allows users without a computational background to transform raw sequence data into normalized contact maps with one click. Juicer produces a hic file containing compressed contact matrices at many resolutions, facilitating visualization and analysis at multiple scales. Structural features, such as loops and domains, are automatically annotated. Juicer is available as open source software at http://aidenlab.org/juicer/.

Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments

Generalized Linear Models (2nd ed.)

HiC-Pro is an optimized and flexible pipeline for processing Hi-C data from raw reads to normalized contact maps. HiC-Pro maps reads, detects valid ligation products, performs quality controls and generates intra- and inter-chromosomal contact maps. It includes a fast implementation of the iterative correction method and is based on a memory-efficient data format for Hi-C contact maps. In addition, HiC-Pro can use phased genotype data to build allele-specific contact maps. We applied HiC-Pro to different Hi-C datasets, demonstrating its ability to easily process large data in a reasonable time. Source code and documentation are available at http://github.com/nservant/HiC-Pro
 .

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HiC-Pro: an optimized and flexible pipeline for Hi-C data processing

We present Rsubread, a Bioconductor software package that provides high-performance alignment and read counting functions for RNA-seq reads. Rsubread is based on the successful Subread suite with the added ease-of-use of the R programming environment, creating a matrix of read counts directly as an R object ready for downstream analysis. It integrates read mapping and quantification in a single package and has no software dependencies other than R itself. We demonstrate Rsubread's ability to detect exon-exon junctions de novo and to quantify expression at the level of either genes, exons or exon junctions. The resulting read counts can be input directly into a wide range of downstream statistical analyses using other Bioconductor packages. Using SEQC data and simulations, we compare Rsubread to TopHat2, STAR and HTSeq as well as to counting functions in the Bioconductor infrastructure packages. We consider the performance of these tools on the combined quantification task starting from raw sequence reads through to summary counts, and in particular evaluate the performance of different combinations of alignment and counting algorithms. We show that Rsubread is faster and uses less memory than competitor tools and produces read count summaries that more accurately correlate with true values.

/pdf/the-r-package-rsubread-is-easier-faster-cheaper-and-better-sk9dwx6k0y.pdf

The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads.

Publisher Correction: Patterns in soil microbial diversity across Europe

Reproductive traits in plants tend to evolve rapidly due to various causes that include plant-pollinator coevolution and pollen competition, but the genomic basis of reproductive trait evolution is still largely unknown. To characterise evolutionary patterns of genome wide gene expression in reproductive tissues and to compare them to developmental stages of the sporophyte, we analysed evolutionary conservation and genetic diversity of protein-coding genes using microarray-based transcriptome data from three plant species, Arabidopsis thaliana, rice (Oryza sativa) and soybean (Glycine max). In all three species a significant shift in gene expression occurs during gametogenesis in which genes of younger evolutionary age and higher genetic diversity contribute significantly more to the transcriptome than in other stages. We refer to this phenomenon as 'evolutionary bulge'' during plant reproductive development because it differentiates the gametophyte from the sporophyte. The extent of the bulge pattern is much stronger than the transcriptomic hourglass, which postulates that during early embryo development an increased proportion of ancient and conserved genes contribute to the total transcriptome. In the three plant species, we observed an hourglass pattern only in A. thaliana but not in rice or soybean, which suggests that unlike the evolutionary bulge of reproductive genes the transcriptomic hourglass is not a general pattern of plant embryogenesis, which is consistent with the absence of a morphologically defined phylotypic stage in plant development.

https://www.biorxiv.org/content/biorxiv/early/2015/07/21/022939.full.pdf

Plant reproductive development is characterised by a transcriptomic evolutionary bulge

Factors driving microbial community composition and diversity are well established but the relationship with microbial functioning is poorly understood, especially at large scales. We analysed microbial biodiversity metrics and distribution of potential functional groups along a gradient of increasing land-use perturbation, detecting over 79,000 bacterial and 25,000 fungal OTUs in 715 sites across 24 European countries. We found the lowest bacterial and fungal diversity in less-disturbed environments (woodlands) compared to grasslands and highly-disturbed environments (croplands). Highly-disturbed environments contain significantly more bacterial chemoheterotrophs, harbour a higher proportion of fungal plant pathogens and saprotrophs, and have less beneficial fungal plant symbionts compared to woodlands and extensively-managed grasslands. Spatial patterns of microbial communities and predicted functions are best explained when interactions among the major determinants (vegetation cover, climate, soil properties) are considered. We propose guidelines for environmental policy actions and argue that taxonomical and functional diversity should be considered simultaneously for monitoring purposes. 

/pdf/patterns-in-soil-microbial-diversity-across-europe-1ysrbxr6.pdf

Patterns in soil microbial diversity across Europe

https://doi.org/10.1016/j.dib.2022.108710

Marc W. Schmid

Papers

Publisher Correction: Patterns in soil microbial diversity across Europe

Plant reproductive development is characterised by a transcriptomic evolutionary bulge

Patterns in soil microbial diversity across Europe

Heavy metal tolerance in Scopelophila cataractae: Transcriptomic and epigenetic datasets