The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V−SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online (http://bioinf.spbau.ru/spades). It is distributed as open source software.

/pdf/spades-a-new-genome-assembly-algorithm-and-its-applications-35zec7ea02.pdf

SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing

HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.

/pdf/hisat-a-fast-spliced-aligner-with-low-memory-requirements-43rbvuq6rk.pdf

HISAT: a fast spliced aligner with low memory requirements

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Motivation: Next-generation sequencing technologies generate very large numbers of short reads. Even with very deep genome coverage, short read lengths cause problems in de novo assemblies. The use of paired-end libraries with a fragment size shorter than twice the read length provides an opportunity to generate much longer reads by overlapping and merging read pairs before assembling a genome.

Results: We present FLASH, a fast computational tool to extend the length of short reads by overlapping paired-end reads from fragment libraries that are sufficiently short. We tested the correctness of the tool on one million simulated read pairs, and we then applied it as a pre-processor for genome assemblies of Illumina reads from the bacterium Staphylococcus aureus and human chromosome 14. FLASH correctly extended and merged reads >99% of the time on simulated reads with an error rate of <1%. With adequately set parameters, FLASH correctly merged reads over 90% of the time even when the reads contained up to 5% errors. When FLASH was used to extend reads prior to assembly, the resulting assemblies had substantially greater N50 lengths for both contigs and scaffolds.

Availability and Implementation: The FLASH system is implemented in C and is freely available as open-source code at http://www.cbcb.umd.edu/software/flash.

Contact: moc.liamg@cogam.t

/pdf/flash-fast-length-adjustment-of-short-reads-to-improve-3pa5wk6ell.pdf

FLASH: Fast Length Adjustment of Short Reads to Improve Genome Assemblies

De novo assembly of RNA-seq data enables researchers to study transcriptomes without the need for a genome sequence; this approach can be usefully applied, for instance, in research on 'non-model organisms' of ecological and evolutionary importance, cancer samples or the microbiome. In this protocol we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-seq data in non-model organisms. We also present Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes. In the procedure, we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from http://trinityrnaseq.sourceforge.net. The run time of this protocol is highly dependent on the size and complexity of data to be analyzed. The example data set analyzed in the procedure detailed herein can be processed in less than 5 h.

http://genomebio.org/wp-content/uploads/2013/08/Haas_2013_Nature_Protocols-1.pdf

De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis

1 Abstract Single cell ATAC-seq (scATAC) shows great promise for studying cellular heterogeneity in epigenetic landscapes, but there remain significant challenges in the analysis of scATAC data due to the inherent high dimensionality and sparsity. Here we introduce scBasset, a sequence-based convolutional neural network method to model scATAC data. We show that by leveraging the DNA sequence information underlying accessibility peaks and the expressiveness of a neural network model, scBasset achieves state-of-the-art performance across a variety of tasks on scATAC and single cell multiome datasets, including cell type identification, scATAC profile denoising, data integration across assays, and transcription factor activity inference.

/pdf/scbasset-sequence-based-modeling-of-single-cell-atac-seq-32599wtelr.pdf

scBasset: Sequence-based modeling of single cell ATAC-seq using convolutional neural networks

Author Correction: scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks

39 untranslated regions (39 UTRs) post-transcriptionally regulate mRNA stability, localization, and translation rate. While 39-UTR isoforms have been globally quantified in limited cell types using bulk measurements, their differential usage among cell types during mammalian development remains poorly characterized. In this study, we examined a dataset comprising ~2 million cells spanning E9.5-E13.5 of mouse embryonic development to quantify transcriptome-wide changes in alternative polyadenylation (APA). We observe a global lengthening of 39 UTRs across embryonic stages in all cell types, although we detect shorter 39 UTRs in hematopoietic lineages and longer 39 UTRs in neuronal cell types within each stage. While the majority of individual genes possess 39 UTRs that lengthen with time, a subset appear to be spatiotemporally regulated through APA. By measuring 39-UTR isoforms in an expansive single cell dataset, our work provides a transcriptome-wide and organism-wide map of the dynamic landscape of alternative polyadenylation during mammalian organogenesis.

/pdf/the-landscape-of-alternative-polyadenylation-in-single-cells-4gy97uv0ot.pdf

The landscape of alternative polyadenylation in single cells of the developing mouse embryo

Replicative senescence (RS) as a model has become the central focus of research into cellular aging in vitro. Despite decades of study, this process through which cells cease dividing is not fully understood in culture, and even much less so in vivo during development and with aging. Here, we revisit Hayflick’s original observation of RS in WI-38 human fetal lung fibroblasts equipped with a battery of high dimensional modern techniques and analytical methods to deeply profile the process of RS across each aspect of the central dogma and beyond. We applied and integrated RNA-seq, proteomics, metabolomics, and ATAC-seq to a high resolution RS time course. We found that the transcriptional changes that underlie RS manifest early, gradually increase, and correspond to a concomitant global increase in accessibility in nucleolar and lamin associated domains. During RS WI-38 fibroblast gene expression patterns acquire a striking resemblance to those of myofibroblasts in a process similar to the epithelial to mesenchymal transition (EMT). This observation is supported at the transcriptional, proteomic, and metabolomic levels of cellular biology. In addition, we provide evidence suggesting that this conversion is regulated by the transcription factors YAP1/TEAD1 and the signaling molecule TGF-β2.

/pdf/revisiting-the-hayflick-limit-insights-from-an-integrated-3fqh4mtx2n.pdf

Revisiting the Hayflick Limit: Insights from an Integrated Analysis of Changing Transcripts, Proteins, Metabolites and Chromatin

(1969). Effect of Selected Treatments upon the Treadmill Running Success of Male Rats. Research Quarterly. American Association for Health, Physical Education and Recreation: Vol. 40, No. 1, pp. 230-234.

David R. Kelley

Papers

scBasset: Sequence-based modeling of single cell ATAC-seq using convolutional neural networks

Author Correction: scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks

The landscape of alternative polyadenylation in single cells of the developing mouse embryo

Revisiting the Hayflick Limit: Insights from an Integrated Analysis of Changing Transcripts, Proteins, Metabolites and Chromatin

Effect of selected treatments upon the treadmill running success of male rats.