UCSC Known Genes Version 3 Take 9

1
UCSC Known Genes Version 3Take 9
2
Known Gene History

• Initially based on Genie predictions constrained
  by BLAT mRNA alignments.
• David Kulp got busy at Affy.
• Switched to RefSeq
• Jim got paranoid Riken RNAs would take over
• Fan built KG 1
• Mark got annoyed at low quality predictions
• Fan Mark built KG 2
• Jim got annoyed at missing genes
• KG 3
• The perfect set until KG 4.

3
Overall Pipeline

• Get alignments etc. from database
• Remove antibody fragments
• Clean alignments, project to genome
• Cluster into splicing graph
• Add EST, Exoniphy, OrthoSplice info.
• Walk unique transcripts out of graph.
• Assign coding regions (CDS) to transcripts.
• Classify into coding, antisense, noncoding.
• Remove weak transcripts.
• Assign accessions.
• Build gene-centric database tables.

4
Genbank Alignment Issues

• Using global instead of local near-best
  alignment, also higher stringency.
• Including all Genbank RNA, not just mRNA
• These changes not yet reflected in Genbank
  mRNA/RefSeq tracks.
• Collect data such as selenocysteine substitutions
  and alternative start codons from Genbank. These
  data are in the .ra files but not the SQL
  database.

5
Removing Antibody Var Regions

• Chromosomes 2,14,22 contain antibody regions.
• Thousands of transcripts for these in Genbank.
• Gaps are from genomic rearrangements, not
  splicing. Millions of possibilities.
• Identify regions by
• Searching for words like immunoglobulin
  variable to make initial set of Ab fragments.
• Treat anything that overlaps these as Ab fragment
  too.
• Cluster together putative Ab fragments.
• Take 4 largest clusters as the 4 variable
  regions. (One is just a pseudogene of a real
  variable region.)
• Remove all alignments in Ab clusters.
• Replace with a single noncoding gene for each
  cluster near end of gene build.

6
Chr22 Ab Region (lambda light chain)
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Cleaning, projecting alignments

• BLAT sometimes leaves messy gappy ends.
• New heuristic
• For gaps 6 base or less on both mRNA and genome,
  just ignore gap, filling in with genome if
  necessary.
• Try to turn other gaps into introns if they are
  not already by wiggling one base on either side
  of gap.
• Break up alignments at remaining gaps that are
  not intronic. Intronic gaps are at least 16
  bases, and have gt/ag or gc/ag ends.
• After break up throw away any pieces less than 18
  bases long.
• For refSeq mRNA only, join pieces back together
  after breaking up. Other mRNA can be joined by
  other transcripts (which may not suffer the same
  problems from polymorphism/error)
• Consider applying similar heuristic in mRNA
  track.

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Cleaning and projecting
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Cluster into splicing graph

• Make graph where vertices are begin/ends of
  exons, edges are exons and introns.
• Multiple input transcripts can share vertices and
  edges.
• Went over this in some detail a few weeks back

10
Splicing graph and txWalk
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Adding Evidence to Graph

• Initial evidence for each edge comes from mRNAs.
• If edge is supported by at least 2 ESTs. (Single
  EST likely is same clone as single RNA) Just
  use spliced ESTs
• Make graph in mouse and map via chains. Reinforce
  orthologous human edges.
• Reinforce exon edges that overlap Exoniphy
  predictions.
• Evidence weight refSeq 100, each mRNA 2, est
  pair 1, mouse ortho 1, exoniphy 1.

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Walking graph

• Weight of 3 on an edge is good enough.
• Rank input RNA by whether refSeq, and number of
  good edges they use.
• If any good edges, output a transcript consisting
  of the edges used by the first RNA.
• Output transcript based on next RNA if the good
  edges it uses have not been output in same order
  before.
• Continue until reach last RNA.

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Evidence, Walk, AltSplice
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Assigning Coding Regions

• Align UniProt and RefSeq proteins to txWalk
  transcripts. Mark regions they hit as possible
  CDS.
• Align Genbank/RefSeq RNAs to txWalk transcripts,
  map CDS from RNA records as possible CDS.
• Use bestorf program for another possible CDS.
• Assign an ad-hoc score to each possible CDS,
  choose highest scoring.
• More comparative genomics could really help here
  someday

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CDS Mapping, Filtering
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(No Transcript)
17
(No Transcript)
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Classifying and Weeding

• The transcripts are classified into
• Coding CDS survives trimming stage
• Near-coding overlap coding by at least 20 bases
  on same strand
• Antisense overlap coding by at least 20 bases on
  opposite strand
• Noncoding other transcripts
• Near-coding transcripts that show signs of
  incomplete splicing (retained intron, bleeds gt
  100 bases into intron) are removed.

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Assigning accessions

• Initial temporary identifiers of form
  ltchromgt.ltclustergt.lttxgt.ltaccessiongt, eg
  chr22.210.5.AB209301
• Make permanent identifiers of form TX12345678.
• Find exact match in previous gene set, and reuse
  previous accession.
• Find compatible match (all introns alike) in old
  gene set, reuse accession, bump version.
• Make up new accession otherwise.
• Record genes in old set not in new.
• Version 7 -gt version 9 mapping actually a good
  test of this 53025 exact, 4732 lost, 3736 new,
  464 compatible.
• Move to UC1234567 format in v. 10?

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Building gene-centric tables

• mmBlastTab, rnBlastTab etc. homolog tables.
  Blastp best plus syntenic weeding.
• kgXref and knownToXxx tables to relate gene to
  other databases and tables.
• kgAlias table to help search on gene names.
• gnfAtlas2Distance to measure expression
  similarity between genes for Gene Sorter. 3 other
  expression distance tables
• humanVidalP2P and humanWankerP2P protein network
  distance tables.
• knownCanonical/knownIsoform tables to help people
  selectively view alt-splicing.
• pbXXX tables for proteome browser.
• In all about 10 hours of compute and indexing.

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The Plan

• Next week
• test preliminary integration on hg18a
• resolve issues with proteome browser
• Tinker on take 10, maybe take 11
• Week after
• Integration of final gene build into hg18a
• Move hg18.knownGenes to hg18.knownGenesOld
• Swap hg18a tables into hg18.
• Coming months
• Continue to improve gene build.
• Add new information from build into details
  pages.
• Allow user filtering of which genes are shown
• Allowing selection by names as well as IDs in
  table browser.
• Present at Cold Spring Harbor. Write up paper.