Table 1 Bioinformatics tools for branch point analyses, Human Splicing Finder (HSF), SVM-BPfinder, Branch Point Prediction (BPP), Branchpointer, LaBranchoR, RNA Branch Point Selection (RNABPS), with their main features and their accessibility

HSF

• Position weighted matrix of 7-mers (YNYCRAY)

DNA sequences¹ or variants¹ (nomenclature HGVS²)

Available as a web-application http://www.umd.be/HSF3/

[14]

• Train on conserved sequences from the Ensembl transcripts

SVM-BPfinder

• Support vector machine combining BP predictions and PPT³ features

DNA sequences (between 20 and 500 nt length)

Available as a web-application + Perl script http://regulatorygenomics.upf.edu/Software/SVM_BP/

[15]

• Train on conserved sequences from 7 mammalian species (with Human)

BPP

• Mixture model combining BP predictions and PPT³ features

DNA sequences (unlimited sequence length)

Available as a python script https://github.com/zhqingit/BPP

[18]

• Train on conserved sequences from human introns

Branchpointer

• Machine learning taking into account the primary and secondary structure of the RNA molecule

Text files with genomic coordinates (format defined by Branchpointer)

Available as an R Bioconductor package https://www.bioconductor.org/packages/release/bioc/html/branchpointer.html

[19]

• Train on high-confidence BPs [10]

LaBranchoR

• Deep learning based on bidirectional LSTM⁴ network

DNA sequences (70 nt upstream of the di-nucleotide AG)

Available as a python script + UCSC genome browser

http://bejerano.stanford.edu/labranchor/

[20]

• Train on high-confidence BPs [10]

RNABPS

• Deep learning based on dilated convolution and bidirectional LSTM⁴ network

DNA sequences (70 nt upstream of the di-nucleotide AG)

Available as a web-application https://home.jbnu.ac.kr/NSCL/rnabps.htm

[21]

• Train on high-confidence BPs [10] plus [16]