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  • MiXCR is an ultimate software platform for analysis of Next-Generation Sequencing (NGS) data for immune profiling. It supports all kinds of single cell platforms and technologies for immune profiling, including commercial vendors such as 10x Genomics or BD Rhapsody and any custom protocol (droplet-based, plate-based, combinatorial barcoding etc.). Check more at https://docs.milaboratories.com.

    • Publications
    • "Antigen receptor repertoire profiling from RNA-seq data"
      DOI: 10.1038/nbt.3979, Published: 2017-10-11, Citations: 276
    • "MiXCR: software for comprehensive adaptive immunity profiling"
      DOI: 10.1038/nmeth.3364, Published: 2015-04-29, Citations: 1504
  • Platform: Java/Kotlin
  • Code: https://github.com/milaboratory/mixcr
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  • License: Custom
  • Categories: Alignment, Allele Specific, Assembly, Clustering, Immune, UMIs
  • Added: 2022-12-25, Updated: 2022-12-25

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  • R package providing functions for fitting, analyzing and visualizing single-cell RNASeq data which has been quantified by counting UMIs while accounting for different sequencing depths/detection rates between cells.
  • Platform: R
  • Code: https://github.com/tallulandrews/PoissonUMIs
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  • License: GPL-2.0
  • Categories: UMIs, Visualisation
  • Added: 2016-10-10, Updated: 2017-09-25

  • The REpertoire Sequencing TOolkit (pRESTO) is composed of a suite of utilities to handle all stages of raw sequence processing prior to alignment against reference germline sequences (germline segment assignment). This tool is part of the Immcantation software suite.

    • Publications
    • "pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires"
      DOI: 10.1093/bioinformatics/btu138, Published: 2014-03-10, Citations: 449
  • Platform: Python
  • Code: https://github.com/immcantation/presto
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  • License: AGPL-3.0
  • Categories: Alignment, Immune, UMIs
  • Added: 2025-04-12, Updated: 2025-04-12

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  • Salmon produces transcript-level quantification estimates for RNA-seq data. It includes the Alevin pipeline for quantifying droplet scRNA-seq.

    • Publications
    • "Salmon provides fast and bias-aware quantification of transcript expression"
      DOI: 10.1038/nmeth.4197, Published: 2017-03-06, Citations: 10327
    • "A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification"
      DOI: 10.1093/bioinformatics/btaa450, Published: 2020-07-13, Citations: 21
    • "Alevin efficiently estimates accurate gene abundances from dscRNA-seq data"
      DOI: 10.1186/s13059-019-1670-y, Published: 2019-03-27, Citations: 235
    • Preprints
    • "Salmon provides accurate, fast, and bias-aware transcript expression estimates using dual-phase inference"
      DOI: 10.1101/021592, Citations: 100
    • "A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification"
      DOI: 10.1101/2020.04.10.035899, Citations: 0
    • "Alevin efficiently estimates accurate gene abundances from dscRNA-seq data"
      DOI: 10.1101/335000, Citations: 2
  • Platform: C++
  • Code: https://github.com/COMBINE-lab/salmon
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  • License: GPL-3.0
  • Categories: Quantification, UMIs
  • Added: 2018-06-08, Updated: 2020-04-14

  • Sinto is a toolkit for processing aligned single-cell data
  • Platform: Python
  • Code: https://github.com/timoast/sinto
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  • License: MIT
  • Categories: UMIs
  • Added: 2020-05-22, Updated: 2020-05-22

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  • kallisto produces transcript-level quantification estimates for RNA-seq data. It can produce transript compatibility count estimates for scRNA-seq data.

    • Publications
    • "Near-optimal probabilistic RNA-seq quantification"
      DOI: 10.1038/nbt.3519, Published: 2016-04-04, Citations: 8752
    • "Modular, efficient and constant-memory single-cell RNA-seq preprocessing"
      DOI: 10.1038/s41587-021-00870-2, Published: 2021-04-01, Citations: 375
    • "A discriminative learning approach to differential expression analysis for single-cell RNA-seq"
      DOI: 10.1038/s41592-018-0303-9, Published: 2019-01-21, Citations: 141
    • Preprints
    • "Accurate quantification of single-nucleus and single-cell RNA-seq transcripts"
      DOI: 10.1101/2022.12.02.518832, Citations: 14
    • "kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA-seq"
      DOI: 10.1101/2023.11.21.568164, Citations: 21
    • "Identification of transcriptional signatures for cell types from single-cell RNA-Seq"
      DOI: 10.1101/258566, Citations: 11
    • "Modular and efficient pre-processing of single-cell RNA-seq"
      DOI: 10.1101/673285, Citations: 83
    • "Near-optimal RNA-Seq quantification"
      arXiv: 1505.02710, Citations: 0
  • Platform: C/C++
  • Code: https://github.com/pachterlab/kallisto
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  • License: BSD-2-Clause
  • Categories: Quantification, UMIs
  • Added: 2018-06-08, Updated: 2023-11-24

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