transcriptomics notes 2

Chapter 2 EST CLUSTERING AND ASSEMBLY

Edited by- Priya J.A . Densi V.B.,Pooja P.S, Rohit G. S.

Published by- Anand M. B.

Table of contents 1. Introduction, 1 2. EST Clustering, 2 2.1 Algorithms used for similarity measurement, 2 2.2 Types of clustering, 3 2.3 EST clustering databases, 4 3. EST assembling , 4 3.1 EST assembling software, 5 3.2 Cluster joining, 5 3.3 Drawbacks of EST assembling,6 4. Tools, 6 5. References,12

1. Introduction:

ESTs (Expressed Sequence Tags) represent partial sequences of cDNA clones (average _ 360

bp).

Single-pass reads from the 5’ and/or 3’ ends of cDNA clones.

Individual clones are picked from the library, and one sequence is generated from each end

of the cDNA insert.

Thus, each clone normally has a 5_ and 3_ EST associated with it.

The sequences average ~360 bases in length.

Because the ESTs are short, they generally represent only fragments of genes, not complete

coding sequences.

Many sequencing centers have automated the process of EST generation, producing ESTs at

a rapid rate.

ESTs are submitted to all three international sequence databases (GenBank, EMBL,and

DDBJ)




2. EST clustering:

The goal of the clustering process is to incorporate overlapping ESTs which tag the same

transcript of the same gene in a single cluster.

For clustering, we measure the similarity (distance) between any 2 sequences.

The distance is then reduced to a simple binary value: accept or reject two sequences in

the same cluster.

As of mid-2000, GenBank contained just under 1.9 million human EST records.

For example, dbEST contains more than 200 ESTs for human alpha-fetoprotein alone.

2.1 Algorithms used for similarity measurement:

Pairwise alignment algorithms:

1) Smith-Waterman

It is the most sensitive, but time consuming (ex. cross-match)

2) Heuristic algorithms,

As BLAST and FASTA, trade some sensitivity for speed.




Non-alignment based scoring methods:

1) d2 cluster algorithm:

It is based on word comparison and composition (word identity

and multiplicity)

Pre-indexing methods.

Purpose-built alignments based clustering methods.

2.2 Types of clustering:

CLUSTERING TYPES

STRINGENT CLUSTERING: LOOSE CLUSTERING:

1) Greater initial fidelity; 1) Lower initial fidelity;

2) One pass; 2) Multi-pass;

3) Lower coverage of expressed gene data; 3) Greater coverage of expressed gene

data;

4) Lower cluster inclusion of 4) Greater cluster inclusion of alternate

expressed gene forms; expressed forms.

5) Shorter consensi. 5) Longer consensi;

6) Risk to include paralogs in the

same gene index.




CLUSTERING

SUPERVISED EST CLUSTERING UNSUPERVISED EST CLUSTERING

ESTs are classified with respect to known ESTs are classified without any prior

sequences or seeds. knowledge.

2.3 EST clustering databases:

EST clustering databases include three databases:

UniGene

TGI (TIGR Gene Index)

STACK

trEST

A combination of supervised and unsupervised methods with variable levels of

stringency are used in UniGene. No consensus sequences are produced.

TIGR Gene Index uses a stringent and supervised clustering method, which generate

shorter consensus sequences and separate splice variants.

STACK uses a loose and unsupervised clustering method, producing longer consensus

sequences and including splice variants in the same index.

trEST is an attempt to produce contigs from clusters of ESTs and to translate

them into proteins.

3. EST assembling:

A multiple alignment for each cluster can be generated, this is known as clustering and

consensus sequences generated which is known as processing.




Assembly and processing result in the production of consensus sequences and

Singletons.

Since ESTs represent gene transcripts, they will not contain repeats.

EST assembly is complicated by features like (cis-)

alternative splicing,

trans-splicing,

single-nucleotide polymorphism,

recoding,

Post-transcriptional modification.

These differences make the new generation assemblers less applicable to EST assembly.

3.1 EST assembling software:

PHRAP VIEW

It provides a ‘‘global’ view of the assembly, complementing the individual base and

trace view provided by consed.

GAP4

Gap4 is an interactive program used for working on data from sequencing projects.

3.2 Cluster joining:

All ESTs generated from the same cDNA clone correspond to a single gene.

Generally the original cDNA clone information is available (_ 90%).

Using the cDNA clone information and the 5’ and 3’ reads information, clusters

can be joined.

http://en.wikipedia.org/wiki/Alternative_splicing

http://en.wikipedia.org/wiki/Trans-splicing

http://en.wikipedia.org/wiki/Single-nucleotide_polymorphism

http://en.wikipedia.org/wiki/Post-transcriptional_modification




3.3 Drawbacks of EST assembly:

The main drawback of EST assembly is that it does not usually permit the determination of

a complete cDNA sequence, because most genes are too large to be covered by end-

sequencing.

In addition, sequence quality drops towards the end of the sequence reads, which can

prevent assembly programs from joining overlapping sequences into a single contig.

Even when full-length contigs are generated, they are likely to contain errors, especially in

regions where only low-quality data are available.

4. Tools:

4.1 EST clustering tools:

1> WCD:

In order to decide whether two ESTs have a sufficiently large approximate overlap, we

have to decide:

o how long the overlap should be;

o how we measure similarity or difference and




o what the error threshold should be.

All of these are parameters of the clustering process.

In addition, wcd provides a number of different features and ways in which the user can

control clustering. Most of these are parameterisable.

wcd provides two ways of comparing ESTs for overlap. The default distance function

used is the d2 distance function, a biologically validated distance function for EST

comparison, and is particularly insensitive to repeats and rearrangement.

wcd has an efficient, published implementation of the d2 distance function. The user

can specify word length, window size and error threshold.

A memory efficient implementation of edit distance is also provided. The user may give

the penalty matrix and threshold or use the defaults provided.

The computations of d2 and edit distance are both expensive.

wcd provides parameterisable heuristics which filter out unnecessary comparisons and

speed up the clustering. Empirical testing has shown that the default parameters of the

heuristics are very conservative:

They do not affect the quality of the results, while speeding up the clustering by an

order of magnitude.

However, more aggressive parameters can speed up the clustering significantly and

have a much smaller impact on the quality results than small changes to other

parameters. In practice, clustering often has to be performed several times as the user

explores different parameters or isolates problems in the data.

Using aggressive heuristics for this early phase is particularly useful.




2> JESAM:

A multi-stage pipeline was developed to discover all the arrangements of all pairs of

sequences where the alignment could be consistent with the sequences being cognate

and contiguous.

The algorithm's first stages reduce the total number of pairwise sequence alignments

whilst aiming to maintain overlap sensitivity and alignment accuracy.

To ensure that the published alignments were both biologically useful and

mathematically optimal, it was thought necessary to use a dynamic programming

algorithm with a sophisticated gap penalty scheme.

However, computation time makes it impractical to compare all sequences against each

other for large EST datasets with workstation implementations of derivatives of the

Smith-Waterman algorithm.

Specialized hardware was an unacceptable solution due to perceived problems of cost,

availability, portability, and ease of algorithm development.

The JESAM alignment algorithm therefore uses dynamic programming only for the final

alignments relying on the gross overall overlap being easy to find because the goal was

only to discover potentially overlapping subsequences, not distant homologues mutated

apart though millennia

3> TGICL:

TGICL uses stringent pairwise comparisons between input sequences to group those

sharing significant regions of near identity. Individual assembly of each cluster has the

advantage of producing larger, more complete consensus sequences while eliminating

potentially misclustered sequences. In its simplest application, TGICL takes a single

parameter:




An input multi-FASTA file. TGICL’s final output is one or more ACE files containing CAP3

assembliesand a list of singletons. Prior to running TGICL, the input dataset should be

cleaned to remove contaminating sequences, including vector, adapter, and bacterial

sequences, which can lead to misclustering and misassembly.

This can be done using either a stringent program such as Lucy or a sequence trimming

script such as SeqClean with filtering databases such as NCBI’s UniVec.

Known repeats should also be masked using RepeatMasker with the lower-case masking

option; TGICL excludes masked regions during its initial word-hashing phase.

4.2 EST assembly tools:

1> Phrapview:

Phrapview is distributed along with the phrap assembly engine and is a graphical viewer

for phrap assemblies.

It is intended to provide a ‘‘global’ view of the assembly, complementing the individual

base and trace view provided by consed.

This global view focuses on information pertaining to possible incorrectness,

incompleteness, or nonuniqueness of the phrap-generated assembly.

Phrapview displays depth of coverage; forward-reverse read pairs, significant pairwise

matches involving reads in different locations in the assembly, and chimeric reads.

The input to phrapview is a .view file, which is produced by running phrap with the

View option. (Note that phrapview does not perform any of the analyses itself; rather, it

provides a way of displaying a file that contains an already completed analysis of the

project)

. A screen dump for a typical phrapview display of a 40-kb cosmid sequencing project is

shown in Figure.




2> GAP4:

Gap4 is an interactive program used for working on data from sequencing projects. It

contains a comprehensive set of functions, many of which present their results

graphically.




Others, such as the Experiment Suggestion functions, produce textual output ready for

parsing by external programs.

One of its important components, used by many of the other functions, is the consensus

algorithm. The gap4 database does not store the consensus sequence; rather, it is

calculated whenever it is needed. When appropriate, it can be calculated separately for

each strand, and, in the Contig Editor and Contig Joining Editor, it is instantly updated

for each edit made.

When phred-style confidence values are available, the algorithm uses them with strand

and chemistry data to calculate a confidence value for each base in the consensus.

At the end of a project, the algorithm can produce a FASTA-format file or an Experiment

file containing the consensus and its confidence values.

Preprocessing programs used by pregap4 and routines within gap4 can add annotations

to readings (for example, the position of an Alu segment or a custom primer).

Throughout the text, these annotations are referred to as ‘‘tags.’’

3> PHRAP:

phrap is a program for assembling shotgun DNA sequence data. Among other features,

it allowsuse of the entire read and not just the trimmed high quality part,

It uses a combination of user-supplied and internally computed data quality information

to improve assembly accuracy in the presence of repeats

It constructs the contig sequence as a mosaic of the highest quality read segments

rather than a consensus, it provides extensive assembly information to assist in trouble-

shooting assembly problems, and it handles large datasets.

4> CAP3:

The CAP3 program includes a number of improvements and new features.

A capability to clip 58 and 38 lowquality regions of reads is included in the CAP3

program. Base quality values produced by PHREDare used in computation of overlaps




between reads, construction of multiple sequence alignments of reads, and generation

of consensus sequences.

Efficient algorithms are employed to identify and compute overlaps between reads.

Forward–reverse constraints are used to correct assembly errors and link contigs.

Results of CAP3 on four BAC data sets are presented. The performance of CAP3 was

compared with that of PHRAP on a number of BAC data sets.

5 References:

(a) From headings 1 to 3 (By Priya J.A, Densi V.B):

1. Webliography

1> http://www.ch.embnet.org/CoursEMBnet/Pages02/slides/est_clustering.pdf

2> http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1874618

2. Bibliography

1> Bioinformatics- a practical guide to the analysis of genes and proteins. (2nd edition)

By -Andreas D. Baxevanis, B.F. Francis Ouellett, Page nos. - 284, 288, 308 & 311

(b) Heading 4th (By Pooja P.S, Rohit G.S):

1. Webliography

1> http://www.genome.org/cgi/content/full/9/9/868#References

2> http://www.phrap.org/phredphrapconsed.html#block_phrap

3> http://genome.wustl.edu/est/esthmpg.html

2. Bibliography

1> Bioinformatics- a practical guide to the analysis of genes and proteins. (2nd edition)

By -Andreas D. Baxevanis, B.F. Francis Ouellett, Page nos. - 288, 309 & 311

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