Sequence analysis protocols (Curr Protoc Bioinformatics.)
1: Curr Protoc Bioinformatics. 2007 Mar;Chapter 11:Unit11.4.
Assembling genomic DNA sequences with PHRAP.
de la Bastide M, McCombie WR.
Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA.
The PHRAP assembly program provides rapid comparison, alignment, and assembly of
large sets of DNA sequences. PHRAP compares sequences by searching for pairs of
perfectly matching "words" or sequence regions that meet certain criteria. If a
match is found, PHRAP then tries to extend the alignment into overlapping
sections called contigs. PHRAP uses quality values produced by the PHRED
basecaller to strike a balance between tolerance of discrepancies and prevention
of stacking repeat sequences. The PHRAP assembly algorithm is generally used as
part of the PHRED/PHRAP/Consed software suite for sequence analysis. This unit
presents instructions for basic usage of the PHRAP assembler, including
preparation of the input files (Support Protocols 1 and 2) and explanation of
output files (Basic Protocols 1 and 2). Several command line options for changing
the PHRAP assembly parameters are also discussed (Basic Protocol 3).
2: Curr Protoc Bioinformatics. 2007 Jan;Chapter 1:Unit 1.5.
Using the NCBI Map Viewer to browse genomic sequence data.
This unit includes an introduction to the Map Viewer, which describes how to
perform a simple text-based search of genome annotations to view the genomic
context of a gene, navigate along a chromosome, zoom in and out, and change the
displayed maps to hide and show information. It also describes some of NCBI's
sequence-analysis tools, which are provided as links from the Map Viewer. The
Alternate Protocols describe different ways to query the genome sequence, and
also illustrate additional features of the Map Viewer. Alternate Protocol 1 shows
how to perform and interpret the results of a BLAST search against the human
genome. Alternate Protocol 2 demonstrates how to retrieve a list of all genes
between two STS markers. Finally, Alternate Protocol 3 shows how to find all
annotated members of a gene family.
3: Curr Protoc Bioinformatics. 2006 Jan;Chapter 9:Unit 9.5.
Using Apollo to browse and edit genome annotations.
Misra S, Harris N.
University of California, Berkeley, California, USA.
An annotation is any feature that can be tied to genomic sequence, such as an
exon, transcript, promoter, or transposable element. As biological knowledge
increases, annotations of different types need to be added and modified, and
links to other sources of information need to be incorporated, to allow
biologists to easily access all of the available sequence analysis data and
design appropriate experiments. The Apollo genome browser and editor offers
biologists these capabilities. Apollo can display many different types of
computational evidence, such as alignments and similarities based on BLAST
searches (UNITS 3.3 & 3.4), and enables biologists to utilize computational
evidence to create and edit gene models and other genomic features, e.g., using
experimental evidence to refine exon-intron structures predicted by gene
prediction algorithms. This protocol describes simple ways to browse genome
annotation data, as well as techniques for editing annotations and loading data
from different sources.
Apollo sequence annotation editor paper
4: Curr Protoc Bioinformatics. 2004 Feb;Chapter 4:Unit4.9.
GrailEXP and Genome Analysis Pipeline for genome annotation.
Uberbacher EC, Hyatt D, Shah M.
Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
The Basic Protocol describes the use of GrailEXP, the latest version of the gene
finding system from Oak Ridge National Laboratory. GrailEXP provides gene models,
by making use of sequence similarity with Expressed Sequence Tags (ESTs) and
known genes. GrailEXP also provides alternatively spliced constructs for each
gene based on the available EST evidence. The Support Protocol describes the use
of the Genome Analysis Pipeline, a web application which allows users to perform
comprehensive sequence analysis by offering a selection from a wide choice of
supported gene finders, other biological feature finders, and database searches.
5: Curr Protoc Bioinformatics. 2003 May;Chapter 4:Unit4.7.
Application of FirstEF to find promoters and first exons in the human genome.
Ohio State University, Columbus, Ohio, USA.
Predicting first exons and promoters is an important part of gene finding in DNA
sequence analysis. This unit presents FirstEF as a method for predicting the
first exons and promoters. A combines FirstEF predictions with other information
such as cDNA/EST matches.