groHMM: a computational tool for identifying unannotated and cell type-specific transcription units from global run-on sequencing data

Table 1 Parameters of each transcript-calling algorithm tested

Method	Algorithm	Explored parameters	Tested values
groHMM	Hidden-Markov Model	-LtProbB (T): Log probability of the transcribed state to non-transcribed state	50..500
groHMM	Hidden-Markov Model	UTS (σ²): variance in read counts of the non-transcribed state	5..50
SICER	Clustering approach	windowSize: size of the windows to scan the genome width	200..2000
SICER	Clustering approach	gapSize: minimum gap size allowed between windows	1x..10x
HOMER	Transcription model	minBodySize: size of region for transcript body detection	500..5000
HOMER	Transcription model	bodyFold: fold enrichment for new transcript detection	2..20

ISSN: 1471-2105