Brendel Group Presentation: 4 Mar 2013

Transcript reconstruction algorithms available in theTrinity RNA-Seq package

Daniel Standage

Brendel Group, Indiana University

4 Mar 2014

Daniel Standage (Brendel Group @ IU) Trinity Assembly 4 Mar 2014 1 / 24

Introduction RNA-Seq

RNA-Seq

Examination of transcriptomesdeep

effective

affordable



RNA-Seq

High throughput comes at the expense ofcontiguity.



RNA-Seq

High throughput comes at the expense ofcontiguity...well, at least for now.


Introduction Assembly with Trinity

Transcriptome assembly

In the absence of full-length transcript sequences,reconstruct full-length sequences from fragments.



Trinity RNA-Seq



Trinity RNA-Seq

Now with 3 transcript reconstruction modes!Butterfly (default)

--PasaFly

--CuffFly



Review outline

Trinity algorithm

PASA algorithm

Cufflinks algorithm

Discussion


Trinity Inchworm

Step 1: Inchworm

Assemble unique contigs representing transcriptsubsequences.

Often produces dominant isoform in full length, and then just uniqueportions of alternative isoforms.


Trinity Inchworm

Inchworm procedure

1 Create dictionary of k-mers (k = 25)

2 Remove k-mers containing probable errors (based on coverage?)

3 Selects highest occurring k-mer

4 Build contig by extending k-mer (find highest occurring k-mer withk − 1 bp overlap, extend 1 bp), remove k-mer from dictionary

5 Repeat previous step until the contig cannot be extended further,report contig

6 Repeat steps 3-5 until all k-mers are exhausted


Trinity Chrysalis

Step 2: Chrysalis

Group Inchworm contigs, construct de Bruijngraph for each cluster.

Each connected component of the graph corresponds to one or more geneswith shared sequence.


Trinity Chrysalis

Chrysalis procedure

1 Group contigs if they share perfect overlap of k − 1 bp (with readssupporting the overlap)

2 Build de Bruijn graph with k − 1 word size for nodes, k for edges;edges weighted by supporting reads

3 Assign each read to component with which it shares the largestnumber of k-mers


Trinity Butterfly

Step 3: Butterfly

Traverse read-supported paths in each subgraph,enumerate plausible sequences.


Trinity Butterfly

Butterfly procedure

1 Graph simplification: merge consecutive nodes in linear paths,pruning minor deviations

2 Plausible path scoring: identify paths in graph with read supportInitialize DP table with source nodes (no incoming edges)Fill in table by extending path prefixes by one node


PASA

PASA

Program to Assemble Spliced Alignments

designed for ESTs and FL-cDNAs (pre-NGS era)

works on sequence alignments

computes consensus spliced alignments


PASA

PASA algorithm

Input: a set of spliced cDNA alignments AOutput: for each alignment a ∈ A, the largest assembly containing a

1 Sort alignments

2 Test overlapping alignments for compatibility

3 Build DP table, backtrace to find maximal assembly A∗

4 If ∃a′ /∈ A∗, build reciprocal DP table, trace to enumerate additionalassemblies


PASA

PASA algorithm

Recurrences

La = maxb{Ca, Lb + Ca/b}

Ra = maxb{Ca,Rb + Ca/b}

La,Ra: maximum number of cDNAs in an assembly that containsalignment a, starting from left and right (respectively)

Ca: number of a-compatible alignments in the span of a

Ca/b: number of a-compatible alignments in the span of a but not inthe span of b


PASA

PASA algorithm


Cufflinks

Cufflinks

designed for short transcript reads (NGS era)

works on read alignments (mappings)

identifies fewest number of transcripts that “explain” the readmappings


Cufflinks

Cufflinks algorithm

Input: overlap graph G ′ of mapped readsOutput: a minimal path cover of G ′, with each path correspondingto a single assembled transcript

1 Alignments divided into non-overlapping loci

2 Erroneous read alignments removed

3 Compute transitive reduction of G ′, G

4 Construct bipartite graph G ∗ from transitive closure of G ,with edgesweighted by coverage to “phase” distant exons by their coverage

5 Compute minimum-cost maximal matching in G ∗, which correspondsto minimum path cover of G ′


Discussion

Three different construction approaches

Butterfly: enumerate all plausible transcripts with minimal readsupport

PASA: for each alignment, find largest assembly (transcript)containing the alignment

CuffLinks: find minimal assembl(y|ies) that explain the data,using read coverage to “phase” distant exons


Discussion

Next time: comparison of 8 Trinity assemblies

Four assembly settingsButterfly--PasaFly

--CuffFly

Butterfly, --min kmer cov 2

Two input data setsGroomed dataGroomed data with digital normalization


Discussion

Next time: comparison of 8 Trinity assemblies

Hypotheses (transcripts per assembly)

Butterfly > PasaFly > CuffFly

Diginorm > No diginorm


Discussion

Thank you!


Technology

Brendel Group Presentation: 4 Mar 2013