Je, a versatile suite to handle multiplexed NGS libraries with unique molecular identifiers

Abstract

BackgroundThe yield obtained from next generation sequencers has increased almost exponentially in recent years, making sample multiplexing common practice. While barcodes (known sequences of fixed length) primarily encode the sample identity of sequenced DNA fragments, barcodes made of random sequences (Unique Molecular Identifier or UMIs) are often used to distinguish between PCR duplicates and transcript abundance in, for example, single-cell RNA sequencing (scRNA-seq). In paired-end sequencing, different barcodes can be inserted at each fragment end to either increase the number of multiplexed samples in the library or to use one of the barcodes as UMI. Alternatively, UMIs can be combined with the sample barcodes into composite barcodes, or with standard Illumina® indexing. Subsequent analysis must take read duplicates and sample identity into account, by identifying UMIs.ResultsExisting tools do not support these complex barcoding configurations and custom code development is frequently required. Here, we present Je, a suite of tools that accommodates complex barcoding strategies, extracts UMIs and filters read duplicates taking UMIs into account. Using Je on publicly available scRNA-seq and iCLIP data containing UMIs, the number of unique reads increased by up to 36 %, compared to when UMIs are ignored.ConclusionsJe is implemented in JAVA and uses the Picard API. Code, executables and documentation are freely available at http://gbcs.embl.de/Je. Je can also be easily installed in Galaxy through the Galaxy toolshed.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1284-2) contains supplementary material, which is available to authorized users.

Modeling glioblastoma invasion using human brain organoids and single-cell transcriptomics

“…Read quality was evaluated using FastQC. iCELL8 library barcodes from the first 21 bp reads were assigned to the associated nanowell with the Je demultiplexing suite 30 . Remaining primer sequences, Poly-A/T tails and low-quality ends (<25) were trimmed using Cutadapt.…”

Identification of X-chromosomal genes that drive global X-dosage effects in mouse embryonic stem cells

“…Briefly, fastq files were uploaded and demultiplex using the "Je-demultiplex" tool (Girardot et al, 2016) . Reads were mapped to the mm10 mouse reference genome (GRCm38) using "Map with BWA" Durbin, 2009, 2010) .…”

Algorithms for efficiently collapsing reads with Unique Molecular Identifiers

“…Then, the reads at each unique alignment location are independently deduplicated based on the UMI sequences. This is done by tools like UMI-tools [5], zUMIs [7], umis [8], gencore [9], fgbio [10], Picard [11], and Je [12]. It is also possible to skip the alignment step and deduplicate reads directly based on the entire DNA sequence, which includes the UMI (this is done by Calib [13]).…”

“…Though there have been many tools proposed for accurately handling UMI data, there have not been many works focused on discussing algorithms that make the UMI deduplication process more efficient. Many previous tools either deduplicate purely based on unique UMIs without being error tolerant [14,3], or require pairwise comparisons between UMIs (i.e., zUMIs [7], Picard [11], Je [12], older versions of UMI-tools [5], gencore [9], and fgbio [10]), which does not efficiently scale to larger datasets. [5] proposed network-based algorithms for estimating the actual number of UMI, which was implemented in their UMI-tools.…”