Skip to main content

Table 1 Summary of features for the algorithms

From: Evaluation of tools for identifying large copy number variations from ultra-low-coverage whole-genome sequencing data

Feature

BIC-seq2

Canvas

CNVnator

FREEC

HMMcopy

QDNAseq

Language

C++, Perl, R

C#

C++

C++, R

C++, R

R

Input format

BAM

BAM

BAM

BAM, many other

BAM

BAM

Control sample

optional

optional

no

optional

optional

yes

User-defined/built-in window size

built-in

built-in

user

both

user

user

Fixed window size

yes

no

yes

yes

yes

yes

GC-correction

yes

yes

yes

yes

yes

yes

Mappability correction

yes

no

no

yes

yes

yes

Sex-determination

From XY CNVs

From XY CNVs

From XY CNVs

User-specified

From XY CNVs

From XY CNVs. By default, XY excluded.

Segmentation

BIC1

Haar wavelet (default), CBS2

Mean shift

LASSO3

HMM4

CBS2

Version

0.2.4, 0.7.2

1.11.0

0.3.3

11.0

1.20.0

1.14.0

Reference

[20]

[21]

[22]

[23]

[24]

[25]

  1. 1Bayesian information criterion
  2. 2circular binary segmentation
  3. 3least absolute shrinkage and selection operator
  4. 4hidden Markov model