Schema for LTRharvest - LTR Fragments Identified by LTRharvest

Database: DkikGB2 Primary Table: ltrdigest_PBS Row Count: 77 Data last updated: 2022-10-20

field	example	SQL type	info
`bin`	585	`smallint(5) unsigned`	range
`chrom`	AFFH02000602	`varchar(255)`	values
`chromStart`	1728	`int(10) unsigned`	range
`chromEnd`	1739	`int(10) unsigned`	range
`name`	LTR_retrotransposon8_PBS	`varchar(255)`	values
`score`	1000	`int(10) unsigned`	range
`strand`	+	`char(1)`	values

Sample Rows

bin	chrom	chromStart	chromEnd	name	score	strand
585	AFFH02000602	1728	1739	LTR_retrotransposon8_PBS	1000	+
585	AFFH02003683	15579	15594	LTR_retrotransposon36_PBS	1000	+
585	AFFH02004003	9456	9467	LTR_retrotransposon42_PBS	1000	-
585	AFFH02004997	8898	8910	LTR_retrotransposon50_PBS	1000	+
585	AFFH02005557	2787	2800	LTR_retrotransposon85_PBS	1000	+
585	AFFH02005907	12179	12193	LTR_retrotransposon101_PBS	1000	+
585	AFFH02008196	10858	10870	LTR_retrotransposon134_PBS	1000	+
585	KB458701	120	133	LTR_retrotransposon143_PBS	1000	+
585	KB458722	12458	12469	LTR_retrotransposon153_PBS	1000	-
585	KB458769	7261	7273	LTR_retrotransposon162_PBS	1000	-

Note: all start coordinates in our database are 0-based, not 1-based. See explanation here.

LTRharvest (ltrdigest) Track Description


	Description LTRharvest is used to identify putative long terminal repeats (LTR) retrotransposons in the D. kikkawai genome using the following parameters: `-seed 76 -minlenltr 116 -maxlenltr 800 -mindistltr 2280 -maxdistltr 8773 -similar 91 -xdrop 7 -mat 2 -mis -2 -ins -3 -del -3 -mintsd 4 -maxtsd 20 -vic 60 -overlaps best` Additional features within the LTR retrotransposons are annotated by LTRdigest with the following parameters: `-pptradius 30 -pptlen 8 30 -pptrprob 0.97 -uboxlen 3 30 -pptuprob 0.91 -pbsradius 30 -pbsalilen 11 30 -pbsoffset 0 5 -pbstrnaoffset 0 40 -pbsmaxedist 1 -pbsmatchscore 5 -pbsmismatchscore -10 -pbsinsertionscore -20 -pbsdeletionscore -20 -pdomevalcutoff 1e-6 -maxgaplen 50` The subset of candidates that show significant similarity to Pfam protein domains within LTR retrotransposons are selected and then clustered using the ltrclustering program with the following parameters: `-psmall 80 -plarge 30` References Ellinghaus D, Kurtz S, and Willhoeft U. LTRharvest, an efficient and flexible software for de novo detection of LTR retrotransposons. BMC Bioinformatics, 9:18, 2008. Steinbiss S, Willhoeft U, Gremme G, and Kurtz S. Fine-grained annotation and classification of de novo predicted LTR retrotransposons. Nucleic Acids Research, 37(21):7002-7013 (2009). Steinbiss S, Kastens S, and Kurtz S. LTRsift: a graphical user interface for semi-automatic classification and postprocessing of de novo detected LTR retrotransposons. Mob DNA. 2012 Nov 7;3(1):18.

Description

References