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Related Concept Videos

RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

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Genome-wide Analysis of HDAC Inhibitor-mediated Modulation of microRNAs and mRNAs in B Cells Induced to Undergo Class-switch DNA Recombination and Plasma Cell Differentiation
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Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data.

Tommaso Andreani1, Linda M Slot2, Samuel Gabillard3

  • 1AI & Deep Analytics-Omics Data Science, Sanofi, Frankfurt am Main 65926, Germany.

NAR Genomics and Bioinformatics
|July 20, 2022
PubMed
Summary
This summary is machine-generated.

Six methods for reconstructing full-length B-cell receptors (BCRs) from single-cell RNA sequencing (scRNA-seq) were evaluated. BALDR, BASIC, and BRACER showed the best performance, with BASIC excelling on short reads and BRACER/BALDR accurately reconstructing somatic hypermutations.

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Area of Science:

  • Immunology
  • Bioinformatics
  • Genomics

Background:

  • Single-cell RNA sequencing (scRNA-seq) enables BCR repertoire analysis, crucial for understanding immune responses, cancer, and autoimmune diseases.
  • Accurate reconstruction of full-length B-cell receptors (BCRs) from scRNA-seq data is vital for antibody-based drug development and disease research.
  • Existing BCR reconstruction methods lack comprehensive performance assessments regarding sequencing parameters and somatic hypermutations.

Purpose of the Study:

  • To comprehensively evaluate and compare the performance of six BCR reconstruction methods using simulated and experimental scRNA-seq data.
  • To identify factors influencing BCR reconstruction accuracy, including sequencing depth, read length, and somatic hypermutation load.
  • To provide guidelines for selecting the optimal BCR reconstruction method based on specific data characteristics.

Main Methods:

  • Evaluation of six BCR reconstruction algorithms (BALDR, BASIC, BRACER, TRUST4, MiXCR, etc.) on one simulated and three experimental SMART-seq datasets.
  • Assessment of method performance based on accuracy, speed, and ability to handle varying read lengths and somatic hypermutation (SHM) levels.
  • Validation of *in silico*-assembled BCRs by expressing them as monoclonal antibodies to confirm target recognition.

Main Results:

  • BALDR, BASIC, and BRACER demonstrated superior overall performance across diverse datasets and conditions.
  • BASIC showed acceptable performance even with very short read libraries.
  • De novo assembly methods (BRACER, BALDR) were most accurate for BCRs with significant SHMs; TRUST4, MiXCR, and BASIC were the fastest.

Conclusions:

  • The choice of BCR reconstruction method significantly impacts accuracy and efficiency, influenced by data characteristics like read length and SHM frequency.
  • BALDR, BASIC, and BRACER are recommended for general full-length BCR reconstruction, with specific strengths for short reads or high SHM loads.
  • Guidelines are proposed to aid researchers in selecting the most appropriate BCR reconstruction tool for their specific scRNA-seq data and research objectives.