Harnesses genetic variation to determine the genetic identity of each droplet containing a single cell. Demuxlet can detect droplets containing two cells from different individuals (doublets). It implements a statistical model for evaluating the likelihood of observing RNA-seq reads overlapping a set of single nucleotide polymorphisms (SNPs) from each cell- containing droplet.
Allows to decipher the effect of individual perturbations and the marginal contributions of genetic interactions on the level of each transcript, program, and cell state. MIMOSCA was designed to assist those attempting to understand biological dynamics by designing, performing, and analyzing perturbation scRNA-seq experiments. MIMOSCA can be extended to other high dimensional molecular phenotypes or diverse cell metadata.
Provides a way of removing amplification biases, the assumed absolute quantification does not appear to hold true perfectly. Umis is a flexible tool for counting the number of unique molecular identifiers. There are four steps in this method: (i) formatting reads, (ii) filtering noisy cellular barcodes, (iii) pseudo-mapping to cDNAs, and (iv) counting molecular identifiers. The quantitation used in umis handles reads that could come from multiple transcripts by assigning a fractional count to each transcript and then filtering for a minimum count at the end.
CEL-Seq provides its first single-cell, on-chip barcoding method, and we detected gene expression changes accompanying the progression through the cell cycle in mouse fibroblast cells. The pipeline consists of the following steps: (1) demultiplexing: using the barcode from R1 we split R2 reads into their original samples creating a separate file for each sample. Since the unique molecular identifier (UMI) is also read in R1 we extract it and attach it to the R2 read metadata for downstream analysis; (2) mapping: using Bowtie2, we map the reads of the different samples in parallel, cutting the analysis time by roughly the number of available cores; (3) read counting: A modified version of the htseq-count script that supports the identification and elimination of reads sharing the same UMI to generate an accurate molecule count for each feature. We use binomial statistics to convert the number of UMIs into transcript counts. The different steps in the pipeline are wrapped together in a single program with a simple configuration file allowing to control for different run modes.
Processes Chromium single cell 3’ RNA-seq output to align reads, generates gene-cell matrices and performs clustering and gene expression analysis. Cell Ranger combines Chromium-specific algorithms with the widely-used RNA-seq aligner STAR. It is delivered as a single, self-contained tar file that can be unpacked anywhere on the system. The tool includes four pipelines: cellranger mkfastq; cellranger count; cellranger aggr; cellranger reanalyze.
Offers a computational method developed to exclude swapped reads in 10x Genomics experiments. BarcodeSwapping2017 provides a package that enables the continued use of cutting-edge sequencing machines for droplet-based assays. This method permits a cost-effective use of highest-throughput sequencing machines for large-scale droplet scRNA-seq experiments while avoiding the confounding effects of barcode swapping.
Demonstrates the value of properly accounting for errors in unique molecular identifiers (UMIs). UMI-tools removes PCR duplicates and implements a number of different UMI deduplication schemes. It can extract, remove and append UMI sequences from fastq reads. Compared with previous method, this one is superior at estimating the true number of unique molecules. The simulations provide an insight into the impact on quantification accuracy and indicate that application of an error-aware method is even more important with higher sequencing depth.
Provides functions to model unique molecular identifier (UMI) based single cell RNA-seq data and differential expression analysis. NBID is a method that uses negative binomial with independent dispersions. It allows multiple groups to be tested simultaneously, as in the generalized linear model framework. This model can also accept size factors estimated by other methods.
Performs initial pre-processing and analysis of the droplet-based scRNA-seq data. DropEst in composed of three steps: (1) identifier parsing phase; (2) read mapping phase; and (3) filtering and quality control phase. It can characterize the quality of a library using a wide range of diagnostic indicators or filters out artefactual cellular barcodes. This tool provides extensive configuration options to accommodate alternative scRNA-seq protocol designs.
Allows quality control (QC) and analysis components of parallel single cell transcriptome and epigenome data. Dr.seq is a quality control (QC) and analysis pipeline that provides both multifaceted QC reports and cell clustering results. Parallel single cell transcriptome data generated by different technologies can be transformed to the standard input with contained functions. Using relevant commands, the software can also be used to report quality measurements based on four aspects and can generate detailed analysis results for scATAC-seq and Drop-ChIP datasets.
Eliminates biases inherent in raw unique molecular identifier (UMI) counts and produces unbiased and low-noise measurements of transcript abundance. TRUmiCount is able to perform comparisons between different genes, exons, and others genomic features. This algorithm exploits the tree-step bias-correction and phantom-removal in expected read counts. It aims to increase the accuracy of quantitative applications of next generation sequencing (NGS) and can to be used in conjunction with the UMI-tools software.
Processes raw reads to count tables for RNA-seq data using Unique Molecular Identifiers (UMIs). zUMIs is a pipeline applicable for most experimental designs of RNA-seq data, such as single-nuclei sequencing techniques. This method allows for down sampling of reads before summarizing UMIs per feature, which is recommended for cases of highly different read numbers per sample. zUMIs is flexible with respect to the length and sequences of the barcodes (BCs) and UMIs, making it compatible with a large number of protocols.
Allows to obtain high-fidelity mutation profiles and call ultra-rare variants by handling caveats of Unique Molecular Identifier (UMI)-based analysis. MAGERI accounts for polymerase chain reaction (PCR) errors by using a variant quality scoring model. It can handle reads with high error load, indels and random offsets. The tool was able to detect circulating tumor DNA (ctDNA) in peripheral blood of cancer patients. It allows easy and efficient processing of high-throughput sequencing data generated.
Addresses the lack of a comprehensive workflow for processing sequencing data from 3 prime end protocols. scPipe can deal with both unique molecular identifiers (UMIs) and sample barcodes, map reads to the genome and summarizes these results into gene-level counts. It implements a simple outlier-based method for discovering low quality cells and possible doublets to remove from further analysis.
Offers tools to address most barcoding situations with and without unique molecular identifier (UMI) and the identification of polymerase chain reaction (PCR) duplicates based on extracted UMIs. In standard experimental set ups (one barcode per sample, identical barcodes at both fragments’ ends) and using equivalent options, Je demultiplex produced identical results when compared to other demultiplexing tools and performed 3.8 times faster and 4.5 times slower than the popular FASTX and eautils packages.
Allows users to handle sequencing data with unique molecular identifiers (UMIs). Umitools can be used for small RNA-seq data and RNA-seq data. This tool facilitates the processing of data that has incorporated a UMI assuming if the UMI is incorporated as a part of the read.
Identifies and error-corrects barcodes by traversing the de Bruijn graph of circularized barcode k-mers. Sircel counts k-mers in circularized barcodes extracted from the reads. It assigns reads to consensus fingerprints constructed from k-mers. The tool permits to make insertion, deletion, and mismatch errors. It requires a minimal number of user-inputted parameters. Sircel can identify several cyclic paths from the barcode de Bruijn graph.
Allows users to transform raw data from dropSeq/scrbSeq experiment to the final count matrix with QC plots. dropSeqPipe is an open source application that can perform five different tasks: (i) generate fastqc reports of the input data, (ii) obtain the final file for the aligned sorted data, (iii) produce plots based on pre-processing and alignement, (iv) create species plot, and (v) extract the expression data.
Assists in processing single cell RNA-seq (scRNA-seq) FASTQ reads generated by CEL-Seq and CEL-Seq2 protocols. SCRUFF permits users to demultiplexe scRNA-seq FASTQ files, align reads to reference genome using Rsubread, and generate unique molecular identifier (UMI) filtered count matrix. This filtering facilitator is available as an R package.
Schematizes single cell unique molecular identifier (UMI) expression data. sctransform employs a regularized negative binomial regression method to proceed. This tool will be implemented into the Seurat package allowing quality control, investigation and exploration of single cell RNA-seq data.
Models single-cell RNASeq data quantified by counting unique molecular identifiers (UMIs). PoissonUMIs is an R package providing functions for fitting, analyzing and visualizing single-cell RNASeq data while accounting for different sequencing depths/detection rates between cells. It also calculates weights based on information contained in each expression value (i.e. difference from null expectation).
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