UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy

Smith, Tom; Heger, Andreas; Sudbery, Ian

doi:10.1101/gr.209601.116

Cited by 1,510 publications

(1,321 citation statements)

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“…To precisely quantify and remove PCR duplicated reads, adaptors containing short random sequences are ligated to the fragments during library preparation to uniquely tag each RNA fragment prior to PCR amplification. These unique molecular identifiers (UMIs) enable accurate classification of unique and duplicated reads by comparing the mapped genomic coordinates of reads that contain the same UMI …”

Section: Improving the Recovery Rate Of Rnas Prepared For Sequencingmentioning

confidence: 99%

Advances and challenges in the detection of transcriptome‐wide protein–RNA interactions

2017

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RNA binding proteins (RBPs) play key roles in determining cellular behavior by manipulating the processing of target RNAs. Robust methods are required to detect the numerous binding sites of RBPs across the transcriptome. RNA‐immunoprecipitation followed by sequencing (RIP‐seq) and crosslinking followed by immunoprecipitation and sequencing (CLIP‐seq) are state‐of‐the‐art methods used to identify the RNA targets and specific binding sites of RBPs. Historically, CLIP methods have been confounded with challenges such as the requirement for tens of millions of cells per experiment, low RNA yields resulting in libraries that contain a high number of polymerase chain reaction duplicated reads, and technical inconveniences such as radioactive labeling of RNAs. However, recent improvements in the recovery of bound RNAs and the efficiency of converting isolated RNAs into a library for sequencing have enhanced our ability to perform the experiment at scale, from less starting material than has previously been possible, and resulting in high quality datasets for the confident identification of protein binding sites. These, along with additional improvements to protein capture, removal of nonspecific signals, and methods to isolate noncanonical RBP targets have revolutionized the study of RNA processing regulation, and reveal a promising future for mapping the human protein‐RNA regulatory network. WIREs RNA 2018, 9:e1436. doi: 10.1002/wrna.1436This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein–RNA RecognitionRNA Interactions with Proteins and Other Molecules > Protein–RNA Interactions: Functional ImplicationsRNA Methods > RNA Analyses in Cells

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Section: Improving the Recovery Rate Of Rnas Prepared For Sequencingmentioning

confidence: 99%

Advances and challenges in the detection of transcriptome‐wide protein–RNA interactions

2017

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“…The UMIs category has also seen a big increase recently as UMI based protocols have become commonly used and tools designed to handle the extra processing steps required have been developed (UMI-tools 28 , umis 29 , zUMIs 30 ).…”

Section: Figure 3 (A) Categories Of Tools In the Scrna-tools Databasementioning

confidence: 99%

Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

Zappia

Phipson

Oshlack

2017

Preprint

101

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As single-cell RNA-sequencing (scRNA-seq) datasets have become more widespread the number of tools designed to analyse these data has dramatically increased. Navigating the vast sea of tools now available is becoming increasingly challenging for researchers.In order to better facilitate selection of appropriate analysis tools we have been cataloguing and curating new analysis tools, as they become available, in the scRNAtools database (www.scRNA-tools.org). Our database collects a range of information on each scRNA-seq analysis tool and categorises them according to the analysis tasks they perform. Exploration of this database gives insights into the areas of rapid development of analysis methods for scRNA-seq data. We see that many tools are developed to perform tasks specific to scRNA-seq analysis, particularly clustering and ordering of cells. We also find that the scRNA-seq community embraces an open-source approach, with most tools available under open-source licenses and preprints being extensively used as a means to describe methods. The scRNA-tools database provides a valuable resource for researchers embarking on scRNA-seq analysis and as a record of the growth of the field over time.

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“…In particular, in protocols where most of the fragmentation occurs after amplification, it is not straightforward to identify the group of potential duplicate reads in which to assess the UMIs, as true duplicate reads may contain sequences from different portions of the same gene. For this reason, UMI-tools [7] can consider all reads mapping to the same gene as potential duplicates. However, this approach discards relevant transcript-level information contained in the read mapping and tends to over-collapse UMIs.…”

Section: Methodsological Considerations and Comparisonsmentioning

confidence: 99%

“…Each dataset was processed with alevin with default settings (as in Section S1.5) and the runtime and memory usage was compared with that observed for the CellRanger pipeline [2] (v2.1.1) and a custom pipeline using STAR(v2.4.2a) [28], featureCounts(v1. 4.6) [29] and UMI-tools (v0.5.3) [7], which represents a commonly used alignment-based approach, which we refer to as naïve [30]. The runtime for alevin is roughly an order of magnitude faster than CellRanger and naive ( Figure 3).…”

Section: Comparing Alevin To Existing Approachesmentioning

confidence: 99%

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Alevin efficiently estimates accurate gene abundances from dscRNA-seq data

Srivastava

Malik

Smith

et al. 2018

Preprint

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We introduce alevin, an efficient pipeline for gene quantification from dscRNA-seq (droplet-based single-cell RNA-seq) data. Alevin is an end-to-end quantification pipeline that starts from sample-demultiplexed FASTQ files and generates gene-level counts for two popular droplet-based sequencing protocols (drop-seq [1], and 10x-chromium [2]). Importantly, alevin handles all processing internally, avoiding reliance on external pipeline programs, and the need to write large intermediate files to disk. Alevin adopts efficient algorithms for cellular-barcode whitelist generation, cellular-barcode correction, lightweight per-cell UMI deduplication and quantification. This integrated solution allows alevin to process data much faster (typically ∼ 10 times faster) than other approaches, while also working within a reasonable memory budget. This enables full, end-to-end analysis for single-cell human experiment consisting of ∼ 4500 cells with 335 Million reads with 13G of RAM and 8 threads (of an Intel Xeon E5-2699 v4 CPU) in 27 minutes.

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UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy

Cited by 1,510 publications

References 32 publications

Advances and challenges in the detection of transcriptome‐wide protein–RNA interactions

Advances and challenges in the detection of transcriptome‐wide protein–RNA interactions

Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

Alevin efficiently estimates accurate gene abundances from dscRNA-seq data

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