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TTh  11:00-12:15 in Clark S361Profs: Serafim Batzoglou, Gill BejeranoTAs: George Asimenos, Cory McLean

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Lecture 18

Chains & NetsNon-coding Transcripts

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Chaining Alignments• Chaining bridges the gulf between syntenic blocks

and base-by-base alignments. • Local alignments tend to break at transposon

insertions, inversions, duplications, etc.• Global alignments tend to force non-homologous

bases to align.• Chaining is a rigorous way of joining together

local alignments into larger structures.[Jim Kent’s slides]

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Chains join together related local alignments

Protease Regulatory Subunit 3

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Chains• a chain is a sequence of gapless aligned blocks, where there must

be no overlaps of blocks' target or query coords within the chain.• Within a chain, target and query coords are monotonically non-

decreasing. (i.e. always increasing or flat)• double-sided gaps are a new capability (blastz can't do that) that

allow extremely long chains to be constructed.• not just orthologs, but paralogs too, can result in good chains. but

that's useful!• chains should be symmetrical -- e.g. swap human-mouse -> mouse-

human chains, and you should get approx. the same chains as if you chain swapped mouse-human blastz alignments.

• chained blastz alignments are not single-coverage in either target or query unless some subsequent filtering (like netting) is done.

• chain tracks can contain massive pileups when a piece of the target aligns well to many places in the query. Common causes of this include insufficient masking of repeats and high-copy-number genes (or paralogs). [Angie Hinrichs, UCSC wiki]

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Affine penalties are too harsh for long gaps

Log count of gaps vs. size of gaps in mouse/human alignment correlated with sizes of transposon relics. Affine gap scores model red/blue plots as straight lines.

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Before and After Chaining

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Chaining Algorithm

• Input - blocks of gapless alignments from blastz• Dynamic program based on the recurrence

relationship: score(Bi) = max(score(Bj) + match(Bi) - gap(Bi, Bj))

• Uses Miller’s KD-tree algorithm to minimize which parts of dynamic programming graph to traverse. Timing is O(N logN), where N is number of blocks (which is in hundreds of thousands)

j<i

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Netting Alignments

• Commonly multiple mouse alignments can be found for a particular human region, particularly for coding regions.

• Net finds best match mouse match for each human region.

• Highest scoring chains are used first.• Lower scoring chains fill in gaps within

chains inducing a natural hierarchy.

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Net Focuses on Ortholog

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Nets

• a net is a hierarchical collection of chains, with the highest-scoring non-overlapping chains on top, and their gaps filled in where possible by lower-scoring chains, for several levels.

• a net is single-coverage for target but not for query.• because it's single-coverage in the target, it's no longer symmetrical.• the netter has two outputs, one of which we usually ignore: the target-

centric net in query coordinates. The reciprocal best process uses that output: the query-referenced (but target-centric / target single-cov) net is turned back into component chains, and then those are netted to get single coverage in the query too; the two outputs of that netting are reciprocal-best in query and target coords. Reciprocal-best nets are symmetrical again.

• nets do a good job of filtering out massive pileups by collapsing them down to (usually) a single level.

[Angie Hinrichs, UCSC wiki]

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"LiftOver chains" are actually chains extracted from nets, or chains filtered by the netting process. Same-species liftOver chains are generated by a series of scripts that use blat -fastMap as the alignment method.

[Angie Hinrichs, UCSC wiki]

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Before and After Chaining

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Net highlights rearrangements

A large gap in the top level of the net is filled by an inversion containing two genes. Numerous smaller gaps are filled in by local duplications and processed pseudo-genes.

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Useful in finding pseudogenes

Ensembl and Fgenesh++ automatic gene predictions confounded by numerous processed pseudogenes. Domain structure of resulting predicted protein must be interesting!

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Mouse/HumanRearrangement Statistics

Number of rearrangements of given type per megabaseexcluding known transposons.

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A Rearrangement Hot Spot

Rearrangements are not evenly distributed. Roughly 5% of the genome is in hot spots of rearrangements such as this one. This 350,000 base region is between two very long chains on chromosome 7.

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Cautionary Note 1

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Cautionary Note 2

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Same Region…

same in allthe other fish

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Orthology vs. Paralogy

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non coding transcripts

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Human Specific Rapid Evolution

hmr hmr c

100%id 100%id

maximally changed

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Nearest Neighbor Model for RNA Secondary Structure Free Energy at 37 OC:

C G U U U G G GUU

CACAAACG

-2 .0

-2 .1

-0 .9

-0 .9

-1 .8

-1 .6

+ 5 .0

Ghelix = GCGGC + G

GUCA + 2G

UUAA + G

UGAC =

-2.0 kcal/mol - 2.1 kcal/mol + 2x(-0.9) kcal/mol - 1.8 kcal/mol = -7.7 kcal/mol

Ghairpin loop = Ginitiation (6 nucleotides) + GmismatchGGCA =

5.0 kcal/mol - 1.6 kcal/mol = 3.4 kcal/mol

Gtotal = G

hairpin + Ghelix = 3.4 kcal/mol - 7.7 kcal/mol = -4.3 kcal/mol

Mathews, Disney, Childs, Schroeder, Zuker, & Turner. 2004. PNAS 101: 7287.

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Transcripts, transcripts everywhere

Human Genome

Transcribed (Tx)

Tx from both strands

Leaky tx?

Functional?

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