Database: hg19    Primary Table: nscanGene    Row Count: 67,448
Format description: A gene prediction with some additional info.

field	example	SQL type	info	description
bin	585	smallint(5) unsigned	range	Indexing field to speed chromosome range queries.
name	chr1.1.001.a	varchar(255)	values	Name of gene (usually transcript_id from GTF)
chrom	chr1	varchar(255)	values	Reference sequence chromosome or scaffold
strand	-	char(1)	values	+ or - for strand
txStart	14695	int(10) unsigned	range	Transcription start position
txEnd	29617	int(10) unsigned	range	Transcription end position
cdsStart	14695	int(10) unsigned	range	Coding region start
cdsEnd	15844	int(10) unsigned	range	Coding region end
exonCount	5	int(10) unsigned	range	Number of exons
exonStarts	14695,14969,15795,16853,29264,	longblob		Exon start positions
exonEnds	14829,15038,15947,17055,29617,	longblob		Exon end positions
score	0	int(11)	range
name2	chr1.1.001	varchar(255)	values	Alternate name (e.g. gene_id from GTF)
cdsStartStat	cmpl	enum('none','unk','incmpl','cmpl')	values	enum('none','unk','incmpl','cmpl')
cdsEndStat	cmpl	enum('none','unk','incmpl','cmpl')	values	enum('none','unk','incmpl','cmpl')
exonFrames	1,1,0,-1,-1,	longblob		Exon frame {0,1,2}, or -1 if no frame for exon

      hg19.nscanPep.name (via nscanGene.name)

bin	name	chrom	strand	txStart	txEnd	cdsStart	cdsEnd	exonCount	exonStarts	exonEnds	score	name2	cdsStartStat	cdsEndStat	exonFrames
585	chr1.1.001.a	chr1	-	14695	29617	14695	15844	5	14695,14969,15795,16853,29264,	14829,15038,15947,17055,29617,	0	chr1.1.001	cmpl	cmpl	1,1,0,-1,-1,
585	chr1.1.002.a	chr1	+	65886	70108	69090	70008	2	65886,69036,	65971,70108,	0	chr1.1.002	cmpl	cmpl	-1,0,
586	chr1.1.003.a	chr1	-	139626	173945	139626	142957	7	139626,142946,155766,158592,165883,168099,173752,	139696,143011,155831,158674,165942,168165,173945,	0	chr1.1.003	cmpl	cmpl	2,0,-1,-1,-1,-1,-1,
586	chr1.1.004.a	chr1	-	228291	234222	228291	228654	2	228291,234187,	228776,234222,	0	chr1.1.004	cmpl	cmpl	0,-1,
587	chr1.1.005.a	chr1	+	334259	368634	367658	368597	3	334259,342391,367620,	334297,342603,368634,	0	chr1.1.005	cmpl	cmpl	-1,-1,0,
73	chr1.1.006.a	chr1	+	437769	567804	529661	567804	6	437769,439248,465211,470906,529614,567658,	438152,439568,465339,471273,529725,567804,	0	chr1.1.006	cmpl	cmpl	-1,-1,-1,-1,0,1,
588	chr1.pasa.1.a	chr1	-	453828	460462	453977	454166	2	453828,460379,	454166,460462,	0	chr1.pasa.1	cmpl	cmpl	0,-1,
589	chr1.pasa.2.a	chr1	+	564812	564881	564818	564851	1	564812,	564881,	0	chr1.pasa.2	cmpl	cmpl	0,
73	chr1.1.007.a	chr1	-	621095	655449	621095	622034	3	621095,639064,655411,	622072,639169,655449,	0	chr1.1.007	cmpl	cmpl	0,-1,-1,
590	chr1.1.008.a	chr1	-	661154	713962	661154	691467	9	661154,671766,691456,694346,701708,703927,708355,709550,713938,	661191,671910,691521,694503,701767,703993,708487,709660,713962,	0	chr1.1.008	cmpl	cmpl	2,2,0,-1,-1,-1,-1,-1,-1,

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

Description

This track shows gene predictions using the N-SCAN gene structure prediction software provided by the Computational Genomics Lab at Washington University in St. Louis, MO, USA.

Methods

N-SCAN combines biological-signal modeling in the target genome sequence along with information from a multiple-genome alignment to generate de novo gene predictions. It extends the TWINSCAN target-informant genome pair to allow for an arbitrary number of informant sequences as well as richer models of sequence evolution. N-SCAN models the phylogenetic relationships between the aligned genome sequences, context-dependent substitution rates, insertions, and deletions.

Human N-SCAN Human N-SCAN uses mouse (mm9) as the informant and iterative pseudogene masking.

Credits

Thanks to Michael Brent's Computational Genomics Group at Washington University St. Louis for providing this data.

Special thanks for this implementation of N-SCAN to Aaron Tenney in the Brent lab, and Robert Zimmermann, currently at Max F. Perutz Laboratories in Vienna, Austria.

References

Gross SS, Brent MR. Using multiple alignments to improve gene prediction. In Proc. 9th Int'l Conf. on Research in Computational Molecular Biology (RECOMB '05):374-388 and J Comput Biol. 2006 Mar;13(2):379-93.

Korf I, Flicek P, Duan D, Brent MR. Integrating genomic homology into gene structure prediction. Bioinformatics. 2001 Jun 1;17(90001):S140-8.

van Baren MJ, Brent MR. Iterative gene prediction and pseudogene removal improves genome annotation. Genome Res. 2006 May;16(5):678-85.

Haas BJ, Delcher AL, Mount SM, Wortman JR, Smith RK Jr, Hannick LI, Maiti R, Ronning CM, Rusch DB, Town CD et al. Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. Nucleic Acids Res 2003 Oct 1;31(19):5654-66.