Ultraplex: An ultra-fast, flexible, all-in-one fastq demultiplexer
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Illumina HiSeq 4000
ORGANISM(S): Homo sapiens
SUBMITTER: Charlotte Capitanchik
PROVIDER: E-MTAB-10349 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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2020-12-25 | E-MTAB-9332 | biostudies-arrayexpress
Project description:Small RNA sequencing has been performed using eight commonly used breast cancer cell lines by Illumina as part of the Illumina iDEA Challange 2011. Total RNA of the cell lines was isolated and the sequencing library was prepared using Illuminas Small RNA v1.5 library prep protocol. This data was analyzed with Flicker, an add-on to the Illumina Genome Analyzer Pipeline software that allows for the preliminary analysis of small RNA runs on the GA. Flicker was used in this case for: * Trimming: Attempts to trim the adaptor sequence from small RNA reads Trimming is accomplished by aligning the adaptor with each read, both internally (the adaptor is fully contained within the read) and as a suffix (the adaptor overlaps the 3' end of the read). A similarity matrix is used to generate the matching score, taking into account both substitutions and indels. The alignment with the best matching score is returned, and that best alignment is trimmed from the read. A trimming threshold may be specified, and the adaptor is not trimmed unless the best score exceeds the threshold. * Aligning with Iterated ELAND: Attempts to aligns trimmed reads to targets (squashed genomes or databases such as miRBase) using the ELAND short read aligner. Each read is aligned by ELAND with specified alignment targets. ELAND was designed to align reads of a uniform length, and the trimmed reads from a small RNA run are several different lengths. We currently address this by running ELAND for reads of each length: reads are binned by length, and each bin of a given length is aligned against a specified target. Once all bins are aligned against all targets (in this case, gDNA and miRBase), the results are recombined into a single file with all the annotation information. ===== Key to Experiments HCT ID, Lane, Sample ID, Cell Line, ER +/- Status, Reads HCT20061, Lane 1, HS378, MCF7, +, 14,555,390 HCT20062, Lane 2, HS379, MDA-MB-231, -, 15,147,037 HCT20063, Lane 3, HS381, T47D, +, 14,701,254 HCT20064, Lane 4, HS377, BT-20, -, 16,529,823 HCT20065, Lane 5, HS375, BT-474, +, 16,443,361 HCT20066, Lane 6, HS380, MDA-MB-468, -, 16,746,981 HCT20067, Lane 7, HS382, ZR-75-1, +, 17,631,005 HCT20068, Lane 8, HS376, MCF10A, normal, 19,155,607
2016-03-14 | E-MTAB-4539 | biostudies-arrayexpress
Project description:5' RNA-Seq of mRNA from S. oneidensis MR-1 grown aerobically in defined lactate medium One lane of sequence for a 5' RNASeq library from RNA treated with exonuclease to remove degraded transcripts, with single-end 40-nt reads
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Project description:Background Single-cell RNA-sequencing (scRNA-seq) experiments typically analyze hundreds or thousands of cells after amplification of the cDNA. The high throughput is made possible by the early introduction of sample-specific bar codes (BCs), and the amplification bias is alleviated by unique molecular identifiers (UMIs). Thus, the ideal analysis pipeline for scRNA-seq data needs to efficiently tabulate reads according to both BC and UMI. Findings zUMIs is a pipeline that can handle both known and random BCs and also efficiently collapse UMIs, either just for exon mapping reads or for both exon and intron mapping reads. If BC annotation is missing, zUMIs can accurately detect intact cells from the distribution of sequencing reads. Another unique feature of zUMIs is the adaptive downsampling function that facilitates dealing with hugely varying library sizes but also allows the user to evaluate whether the library has been sequenced to saturation. To illustrate the utility of zUMIs, we analyzed a single-nucleus RNA-seq dataset and show that more than 35% of all reads map to introns. Also, we show that these intronic reads are informative about expression levels, significantly increasing the number of detected genes and improving the cluster resolution. Conclusions zUMIs flexibility makes if possible to accommodate data generated with any of the major scRNA-seq protocols that use BCs and UMIs and is the most feature-rich, fast, and user-friendly pipeline to process such scRNA-seq data.
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Project description:In parallel to the genetic code for protein synthesis, a second layer of information is embedded in all RNA transcripts in the form of RNA structure. The ability of RNA to base pair with itself and other nucleic acids endow RNA with the capacity to form extensive structures, which are known to influence practically every step in the gene expression program1. Yet the nature of most RNA structures or effects of sequence variation on structure are not known. Here we report the initial landscape and variation of RNA secondary structures (RSS) in a human family trio, providing a comprehensive RSS map of human coding and noncoding RNAs. We identify unique RSS signatures that demarcate open reading frames, splicing junctions, and define authentic microRNA binding sites. Comparison of native deproteinized RNA isolated from cells versus refolded purified RNA suggests that the majority of the RSS information is encoded within RNA sequence. Over one thousand transcribed single nucleotide variants (~15% of all transcribed SNVs) alter local RNA structure; these “RiboSNitches”2 occur in disease-associated variants. We discover simple sequence and spacing rules that determine the ability of point mutations to impact RSS. Selective depletion of RiboSNitches versus structurally synonymous variants at precise locations suggests selection for specific RNA shapes at thousands of sites, including 3’UTRs, binding sites of miRNAs and RNA binding proteins genome-wide. These results highlight the potentially broad contribution of RNA structure and its variation to gene regulation. RNA structure probing is performed at 37˚C on poly(A)+ selected RNAs from GM12878, GM12891 and GM12892 cell lines, as well as on native proteinized RNAs from GM12878. The structure probed RNAs is then cloned into a sequencing library using modied Ambion RNA sequencing kit compatible with the Illumina platform. The samples were deep sequenced using Illumina's Hi-Seq platform. AGO CLIP was performed as reported. Cells were crosslinked with UV and lysed using published protocols. AGO2 was enriched using immunopurification. The RNA-protein complex was digested with ribonuclease and purified by gel electrophoresis. Purified RNA was reverse transcribed and cDNA molecules were amplified and sequenced as described.
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