Ultraplex- A rapid, flexible, all-in-one fastq demultiplexer [version 1; peer review- 1 approved]

BACKGROUND: The first step of virtually all next generation sequencing analysis involves the splitting of the raw sequencing data into separate files using sample-specific barcodes, a process known as “demultiplexing”. However, we found that existing software for this purpose was either too inflexible or too computationally intensive for fast, streamlined processing of raw, single end fastq files containing combinatorial barcodes. RESULTS: Here, we introduce a fast and uniquely flexible demultiplexer, named Ultraplex, which splits a raw FASTQ file containing barcodes either at a single end or at both 5’ and 3’ ends of reads, trims the sequencing adaptors and low-quality bases, and moves unique molecular identifiers (UMIs) into the read header, allowing subsequent removal of PCR duplicates. Ultraplex is able to perform such single or combinatorial demultiplexing on both single- and paired-end sequencing data, and can process an entire Illumina HiSeq lane, consisting of nearly 500 million reads, in less than 20 minutes. CONCLUSIONS: Ultraplex greatly reduces computational burden and pipeline complexity for the demultiplexing of complex sequencing libraries, such as those produced by various CLIP and ribosome profiling protocols, and is also very user friendly, enabling streamlined, robust data processing. Ultraplex is available on PyPi and Conda and via Github

Analysis of RNA-Seq datasets reveals enrichment of tissue-specific splice variants for nuclear envelope proteins

<p>Laminopathies yield tissue-specific pathologies, yet arise from mutation of ubiquitously-expressed genes. A little investigated hypothesis to explain this is that the mutated proteins or their partners have tissue-specific splice variants. To test this, we analyzed RNA-Seq datasets, finding novel isoforms or isoform tissue-specificity for: Lap2, linked to cardiomyopathy; Nesprin 2, linked to Emery-Dreifuss muscular dystrophy and Lmo7, that regulates the Emery-Dreifuss muscular dystrophy linked emerin gene. Interestingly, the muscle-specific Lmo7 exon is rich in serine phosphorylation motifs, suggesting regulatory function. Muscle-specific splice variants in non-nuclear envelope proteins linked to other muscular dystrophies were also found. Nucleoporins tissue-specific variants were found for Nup54, Nup133, Nup153 and Nup358/RanBP2. RT-PCR confirmed novel Lmo7 and RanBP2 variants and specific knockdown of the Lmo7 variantreduced myogenic index. Nuclear envelope proteins were enriched for tissue-specific splice variants compared to the rest of the genome, suggesting that splice variants contribute to its tissue-specific functions.</p

How Do You Identify m⁶ A Methylation in Transcriptomes at High Resolution? A Comparison of Recent Datasets

A flurry of methods has been developed in recent years to identify N6-methyladenosine (m6A) sites across transcriptomes at high resolution. This raises the need to understand both the common features and those that are unique to each method. Here, we complement the analyses presented in the original papers by reviewing their various technical aspects and comparing the overlap between m6A-methylated messenger RNAs (mRNAs) identified by each. Specifically, we examine eight different methods that identify m6A sites in human cells with high resolution: two antibody-based crosslinking and immunoprecipitation (CLIP) approaches, two using endoribonuclease MazF, one based on deamination, two using Nanopore direct RNA sequencing, and finally, one based on computational predictions. We contrast the respective datasets and discuss the challenges in interpreting the overlap between them, including a prominent expression bias in detected genes. This overview will help guide researchers in making informed choices about using the available data and assist with the design of future experiments to expand our understanding of m6A and its regulation