v1.1.3: Fixed a naming error of 'clonal.tree.generation()'

We have corrected the identifier naming error in the fasta generated by clonal.tree.generation(). Previously, we used 'clonotype_index' to name identifiers, where each 'clonotype_index' represents a clonotype composed of the same IGHV gene, IGHJ gene and junction amino acid sequence. However, when constructing the clonal tree, we used the DNA sequence of the raw data, so the identifier should be named using 'sequence_id'.

v1.1.2: Allow filtering of low-frequency sequences during data preprocessing

We updated the data.pro() function to allow filtering out low-frequency sequences (which may have arisen due to sequencing errors) during data preprocessing. Users can define the filtering threshold by adjusting 'count_filter_thre'. It defaults to 'NA' which means no filtering is performed.

v1.1.1: Increase gap opening penalty of MSA

To obtain more accurate multiple sequence alignment results, we increased the gap opening penalty for amino acid sequences from 10 (default) to 20, thereby minimizing the addition of gaps in clusters with equal-length sequences.

v1.1

We introduce an R-based computational pipeline meticulously designed for the efficient analysis of BCR repertoire sequencing data. The pipeline is grounded in fastBCR, a heuristic algorithm tailored for prompt clonal family inference. Its capabilities are further enriched through the integration of a comprehensive suite of essential modules. These modules encompass V/J gene usage statistics, distribution of conserved motifs, construction of phylogenetic trees, analysis of affinity maturation, diversity assessment, and the capability to query neutralizing antibodies (NAbs). Moreover, we offer methodologies for scrutinizing variations in clonal family compositions across diverse groups. This comprehensive pipeline advances the computational analyses of BCR repertoire, offering a convenient and effective approach for the systematic investigation and understanding of the B cell immune response.

v1.0

fastBCR is an efficient tool tailored for B cell receptor (BCR) clonal family inference, particularly from extensive BCR repertoire datasets. This tool contributes to advancing our comprehension of B cell activation and antibody-related investigations. Its primary objective is the prompt identification of B cell clonal families within vast BCR heavy chain sequences. To further bolster post-clustering analysis, fastBCR offers an array of functional modules. These include multiple sequence alignment (MSA), phylogenetic tree construction, somatic hypermutation (SHM) evaluation, and class switch analysis (CSR). Additionally, fastBCR incorporates a BCR simulation module that aids in generating variable B cell clonal lineages with distinct mutation rates. These modules enhance the applicability of fastBCR in BCR repertoire analysis and the exploration of antibodies, making it a valuable asset in immunological research.