BLAST & Genome assembly - Scientific Computing – HITS … · · 2013-11-15Introduction...

Introduction BLAST Genome assembly Conclusion

BLAST & Genome assembly

Solon P. Pissis Tomas Flouri

Heidelberg Institute for Theoretical Studies

November 17, 2012

1 IntroductionIntroduction

2 BLASTWhat is BLAST?The algorithm

3 Genome assemblyDe novo assemblyMapping assembly

4 ConclusionOverview

Contents

1 Introduction

2 BLAST

3 Genome assembly

4 Conclusion

Introduction

Sequence alignment is the process of comparing two or morestrings of letters (e.g. nucleotides or amino acids) to infertheir similarity.

Introduction

Pairwise sequence alignment is the process of comparing onlytwo strings.

Introduction

Useful in dozens of biological applications (SSE- andGPU-based accelerated implementations).

Introduction

BLAST: Basic Local Alignment Search Tool is a set ofprograms for fast approximate comparison of biologicalsequences, such as the amino-acid sequences of differentproteins or the nucleotides of DNA sequences.

Introduction

BLAST: Basic Local Alignment Search Tool is a set ofprograms for fast approximate comparison of biologicalsequences, such as the amino-acid sequences of differentproteins or the nucleotides of DNA sequences.

Genome assembly: taking a huge number of DNA sequencesand putting them back together to create a representation ofthe genome from which the DNA originated.

Contents

1 Introduction

2 BLAST

3 Alignment algorithms on strings

4 Conclusion

What is BLAST?

BLAST: a set of programs

Basic Local Alignment Search Tool is a set of programs forfast and approximate comparison of biological sequences.

What is BLAST?

Basic Local Alignment Search Tool is a set of programs forfast and approximate comparison of biological sequences.In particular, BLAST is useful for the comparison between aquery sequence and a library or database of sequences, inorder to identify library sequences that resemble the querysequence above a certain threshold.

What is BLAST?

Basic Local Alignment Search Tool is a set of programs forfast and approximate comparison of biological sequences.In particular, BLAST is useful for the comparison between aquery sequence and a library or database of sequences, inorder to identify library sequences that resemble the querysequence above a certain threshold.The five traditional BLAST implementations are:

What is BLAST?

BLASTN: both the database and the query are nucleotidesequences

What is BLAST?

BLASTN: both the database and the query are nucleotidesequencesBLASTP: both the database and the query are proteinsequences

What is BLAST?

BLASTN: both the database and the query are nucleotidesequencesBLASTP: both the database and the query are proteinsequencesBLASTX: the database are protein sequences and the query isnucleotide translated into protein sequence

What is BLAST?

BLASTN: both the database and the query are nucleotidesequencesBLASTP: both the database and the query are proteinsequencesBLASTX: the database are protein sequences and the query isnucleotide translated into protein sequenceTBLASTN: the database are nucleotide translated into proteinsequence and the query is a protein sequence

What is BLAST?

BLASTN: both the database and the query are nucleotidesequencesBLASTP: both the database and the query are proteinsequencesBLASTX: the database are protein sequences and the query isnucleotide translated into protein sequenceTBLASTN: the database are nucleotide translated into proteinsequence and the query is a protein sequenceTBLASTX: both the database and the query are nucleotidetranslated into protein sequences

The algorithm

“Why not Smith-Waterman algorithm?”

The algorithm

Smith-Waterman algorithm computes the optimal (maximumscoring) local alignment between two sequences.

The algorithm

In biological applications, we usually need to infer thestatistically significant alignments very fast.

The algorithm

BLAST does not explore the entire search space (DP matrix)but it minimizes the search space for efficiency...

The algorithm

...at the cost of sensitivity

The algorithm

It uses three layers of rules to sequentially identify refinepotential high scoring pairs (HSPs).

The algorithm

These heuristics layers—seeding, extension, andevaluation—form a stepwise refinement procedure.

The algorithm

These heuristics layers—seeding, extension, andevaluation—form a stepwise refinement procedure.

Allows for sampling the entire search space without wastingtime on dissimilar regions.

The algorithm

The algorithm: seeding

BLAST assumes that significant alignments have commonsubwords (substrings or factors) of a fixed-length W .

The algorithm

It first determines the locations of all the common exactmatching substrings which are called word hits.

The algorithm

Only those regions with word hits will be used as alignmentseeds.

The algorithm

In this way BLAST ingores a large fraction of search space.

The algorithm

The neighborhood of a subword contains the word itself andall other words whose score is ≤ T when compared via thesubstitution matrix to the subword.

The algorithm

We may adjust T to control the size of theneighborhood—affecting speed and sensitivity.

The algorithm

We may adjust T to control the size of theneighborhood—affecting speed and sensitivity.

Hence, the interplay between W , T , and the substitutionmatrix is critical!!!

The algorithm

The algorithm: extension

The algorithm

Once the search space is seeded, alignments can be generatedby starting from the individual seeds.

The algorithm

BLAST extends a longer alignment between the query and thedatabase sequence in the left and right direction of the word.

The algorithm

It only searches a subset of the space, so it needs amechanism to know when to stop the extension procedure.

The algorithm

It uses a threshold X representing how much the score isallowed to drop off since the last maximum.

The algorithm

The extension is stopped as soon as the sum score decreasesby more than X when compared with the highest valueobtained during the extension process.

The algorithm

The alignment is trimmed back to the maximum score.

The algorithm

The alignment is trimmed back to the maximum score.

It is generally a good idea to use a large value for X , whichreduces the risk of premature termination.

The algorithm

The algorithm: evaluation

Once seeds have been extended in both directions to createalignments, these alignments are evaluated (post-processed)to determine if they are statistically significant.

The algorithm

The significant alignments are termed HSPs (High ScoringPairs).

The algorithm

At the simplest level we can use an optional alignment scorethreshold (cut-off) S—empirically determined—to sort thealignments into low and high scoring.

The algorithm

At the simplest level we can use an optional alignment scorethreshold (cut-off) S—empirically determined—to sort thealignments into low and high scoring.

By examining the distribution of the alignment scores modeledby comparing random sequences, S can be determined suchthat its value is large enough to guarantee the significance ofthe remaining HSPs.

The algorithm

BLAST next assesses the statistical significance of each HSPscore by using a final threshold.

The algorithm

It computes the probability p of observing a score S equal toor grater than score x by exploiting the Gumbel extreme valuedistribution (GEDV).

The algorithm

It is shown that the distribution of Smith-Waterman localalignment scores between two random sequences followsGEDV.

The algorithm

The computation of p is based on statistical parametersdepending upon the substitution matrix, the gap penalties,and the problem size.

The algorithm

The computation of p is based on statistical parametersdepending upon the substitution matrix, the gap penalties,and the problem size.

The final threshold E (computed by p) of a database match isthe number of times that a random sequence would obtain ascore S higher than x by chance.

Contents

1 Introduction

2 BLAST

3 Genome assembly

4 Conclusion

Genome assembly

Genome assembly is the process of taking a huge number ofDNA sequences and putting them back together to create arepresentation of the genome from which the DNA originated.

Genome assembly

De novo: assembling short reads to createfull-length—sometimes novel—sequences.

Genome assembly

Mapping: assembling reads by aligning them against anexisting reference sequence—building a sequence that issimilar but not necessarily identical to the reference.

Genome assembly

Genome assembly is generally a very difficult computationalproblem, and since 2005, probably, one of the hottests inBioinformatics.

Genome assembly

Genome assembly is generally a very difficult computationalproblem, and since 2005, probably, one of the hottests inBioinformatics.

In terms of time and space complexity, de novo assembly isorders of magnitude slower and more memory intensive thanmapping assembly.

Genome assembly: DNA sequencing

ATTAGCATAC...

DNA sequencing includes several methods and technologiesthat are used for determining the exact order of the nucleotidebases—adenine, guanine, cytosine, and thymine—in a DNAmacromolecule.

The traditional sequencing methods, named after Sanger anddeveloped in the mid 70’s, had been the workhorse technologyfor DNA sequencing for almost thirty years.

With the paramount goal of analysing the human genome, thethroughput demand of DNA sequencing increased by anunexpected magnitude, leading to new developments.

The speed, accuracy, efficiency, and cost-effectiveness ofsequencing technology have been improving since.

Genome assembly: Next-generation sequencing

In 2005: the milestone publication of thesequencing-by-synthesis (SBS) technology (Margulies et al.,2005), and the multiplex polony sequencing protocol ofGeorge Church’s laboratory (Shendure et al., 2005).

Short sequences (reads) of length 25-100 base pairs (bp),which after sixteen months on the market had increased to250 bp.

Recent advances have raised the mark again to more than 500bp—drawing near today’s Sanger sequencing read length of750 bp.

Apart from read length, the massive amount (tens of millions)of sequencing reads that can be produced in a singleinstrument run for a given cost is another important aspect.

These advances is what we call next-generation sequencing(NGS).

Genome assembly: Impact

The impact that these next-generation sequencing innovationswill have in clinical genetics will certainly be crucial.

The low-scale, targeted gene/mutation analysis currentlydominating clinical genetics will ultimately be replaced bylarge-scale sequencing of entire disease gene pathways andnetworks.

Eventually, the perceived clinical benefit of whole-genomesequencing will outweigh the cost of the procedure.

Allowing for these tests to be performed on a routine basis fordiagnostic purposes.

Or perhaps in the form of a screening programme, that couldbe used to guide personalised medical treatments throughoutthe lifetime of the individual.

2M characterized species of plants and animals—notaccounting for microbes; only 3791 completed genomes.

De novo assembly: what is it?

De novo assembly

De novo assembly is a hierarchical data structure that mapsthe sequence data to a putative reconstruction of the target.

De novo assembly

It groups reads into contigs and contigs into scaffolds.

De novo assembly

Contigs provide a multiple sequence alignment of reads plusthe consensus sequence.

De novo assembly

Scaffolds define the contig order and orientation and the sizesof the gaps between contigs using mate pairs (paired-end)information.

De novo assembly

Assemblies are measured by the size and accuracy of theircontigs and scaffolds.

De novo assembly

Assembling a genome using many short NGS reads requires adifferent approach than the methods developed for the fewerbut longer reads produced by Sanger sequencing.

De novo assembly

Assembling a genome using many short NGS reads requires adifferent approach than the methods developed for the fewerbut longer reads produced by Sanger sequencing.

There are two basic algorithmic approaches for de novoassembly: overlap graphs and de Bruijn graphs.

De novo assembly

De novo assembly algorithms: Overlap graphs

Figure: Colored nucleotides indicate overlaps between reads

De novo assembly

Compute all pair-wise overlaps between the reads and capturethis information in a graph.

De novo assembly

Each node in the graph corresponds to a read, and an edgedenotes an overlap between two reads.

De novo assembly

The overlap graph is used to compute an arrangement ofreads and a consensus sequence of contigs.

De novo assembly

This method works best when there is a small number ofreads with significant overlap.

De novo assembly

This method works best when there is a small number ofreads with significant overlap.

Some NGS assemblers use overlap graphs, but this traditionalapproach is computationally intensive: even a de novoassembly of small-sized genomes needs millions of reads,making the overlap graph extremely large.

De novo assembly

Walking along a Hamiltonian cycle (each vertex once) byfollowing the edges in numerical order allows one toreconstruct the genome by combining alignments betweensuccessive reads.

De novo assembly

This method, however, although simple is computationallyextremely expensive.

De novo assembly

A million reads will require a trillion pairwise alignments.

De novo assembly

A million reads will require a trillion pairwise alignments.

There is no known efficient algorithm for finding aHamiltonian cycle.

De novo assembly

De novo assembly algorithms: de Bruijn graphs

Figure: The trick is to construct the de Brujin graph by representing allk-mer prefixes and suffixes as nodes and then drawing edges thatrepresent k-mers having a particular prefix and suffix

De novo assembly

Most NGS assemblers use de Bruijn graphs.

De novo assembly

De Bruijn graphs reduce the computational effort by breakingreads into smaller sequences of DNA, called k-mers, where theparameter k denotes the length in bases of these sequences.

De novo assembly

The de Bruijn graph captures overlaps of length k − 1between these k-mers and not between the actual reads.

De novo assembly

By reducing the entire data set down to k-mer overlaps the deBruijn graph reduces redundancy in short-read data sets(same k-mers are represented by a unique node in the graph).

De novo assembly

The most efficient k-mer size for a particular assembly isdetermined by the read length as well as the error rate; k hassignificant influence on the quality of the assembly.

De novo assembly

The most efficient k-mer size for a particular assembly isdetermined by the read length as well as the error rate; k hassignificant influence on the quality of the assembly.

Another attractive property of de Bruijn graphs is that repeatsin the genome can be collapsed in the graph and do not leadto many spurious overlaps.

De novo assembly

Figure: Relationship between the quality score Q and the probability pthat the corresponding base call is incorrect; using Sanger (red) andSolexa (black) equations.

De novo assembly

Finding an Eulerian cycle (visit each edge once) allows one toreconstruct the genome by forming an alignment in whicheach succesive k-mer (from successive edges) is shifted by oneposition.

De novo assembly

Hence we avoid the computationally expensive task of findinga Hamiltonian cycle.

De novo assembly

As we visit all edges of the de Brujin graph, which representall possible k-mers we can spell out a candidate genome; foreach edge we traverse, we record the first nucleotide of thek-mer assigned to that edge.

De novo assembly

De novo assembly: a note for Computer Scientists

A simple formulation of the de novo assembly problem as anoptimization problem phrases the problem as a classicalproblem of algorithms on strings: the Shortest CommonSuperstring (SCS) problem.

De novo assembly

Input: strings s1, s2, . . . , sk , where si ∈ Σ∗.

De novo assembly

Output: the shortest string s containing each si as a factor.

De novo assembly

e.g. given s1 = abaab, s2 = baba, s3 = aabbb, ands4 = bbab, we want to output s = bbabaabbb.

De novo assembly

e.g. given s1 = abaab, s2 = baba, s3 = aabbb, ands4 = bbab, we want to output s = bbabaabbb.

SCS problem is shown to be NP-complete! (via the TravelingSalesman problem)

Mapping assembly

Mapping assembly: what is it?

ATTAGCATAC...~3GB

Depth 10 * 3GB = 30GB

Mapping assembly

Hundreds of millions of short reads (dozens or hundreds ofGigabytes) must be mapped (aligned) against a reference sequence(3Gb for human).

Mapping assembly

Definition

Given a text t of length n, where t ∈ Σ+, Σ = {A,C,G,T}, a set

{p1, p2, . . . , pr } of patterns, each of length m < n, where pi ∈ Σ+,

for all 1 ≤ i ≤ r , and an integer e < m, find all the factors of t,which are at Hamming distance less than, or equal to, e from pi .

Mapping assembly

Definition

Given a text t of length n, where t ∈ Σ+, Σ = {A,C,G,T}, a set

{p1, p2, . . . , pr } of patterns, each of length m < n, where pi ∈ Σ+,

for all 1 ≤ i ≤ r , and an integer e < m, find all the factors of t,which are at Hamming distance less than, or equal to, e from pi .

where Σ+ denotes the set of all the strings on the alphabet Σ

except the empty string ε.

Mapping assembly

Mapping assembly: why not BLAST?

BLAST reports all significant alignments or typically tens oftop-scoring alignments.

In read mapping, we are typically more interested in the bestalignment or best few alignments, covering each region of thequery sequence.

For example, suppose a 1000 bp query sequence consists of a900 bp segment from one chromosome and a 100 bp segmentfrom another chromosome.

Further, suppose that 400 bp out of the 900 bp segment is ahighly repetitive sequence.

For BLAST, to know this is a chimeric read, we would need toask it to report all the alignments of the 400 bp repeat, whichis costly and wasteful because in general we are not interestedin alignments of short repetitive sequences contained in alonger unique sequence.

Mapping assembly

Mapping assembly: algorithms

The most straightforward way of finding all the occurrences ofa read, if no gap is allowed, consists in sliding the read alongthe genome sequence and noting the positions where thereexists a match.

Mapping assembly

Unfortunately, although conceptually simple, this algorithmhas a huge complexity.

Mapping assembly

When gaps are allowed, one has to resort to traditionaldynamic programming algorithms, such as theNeedleman-Wunsch algorithm.

Mapping assembly

Unfortunately, the complexity becomes even larger.

Mapping assembly

Therefore, to be efficient, all the methods must rely on somesort of pre-processing.

Mapping assembly

Therefore, to be efficient, all the methods must rely on somesort of pre-processing.

i.e. index the genome to provide a direct and fast access to itssubstrings of a given size, using either hashing-based indexesor Burrows-Wheeler-transform-based indexes.

Mapping assembly

Mapping assembly algorithms: hashing

Store the positions of the k-mers in an array of linked lists,using a value of k significantly less than the read size, sayk = 9.

Mapping assembly

In terms of space, the problem is tractable since there are, atmost, 49

= 262144 different 9-mers in the genome.

Mapping assembly

Select a k-mer for each read (a good choice is the leftmostpart, because the quality is better) and map it to the genomeusing the hashing procedure—the seed.

Mapping assembly

For each possible hit, the procedure would then try to mapthe rest (extend) of the read to the genome (possibly allowingerrors, in a Needleman-Wunsch-like algorithm).

Mapping assembly

This two-steps strategy is called seed and extend.

Mapping assembly

This two-steps strategy is called seed and extend.

Drawback is that seeds are usually highly repeated in thereference genome: huge linked lists!

Mapping assembly

A better approach is to divide each read into q equally-longnon-overlapping substrings.

Mapping assembly

Suppose that one allows for e mismatches.

Mapping assembly

At least q − e out of the q substrings can be mapped exactly(in the worst case, the e errors are located in e differentsubstrings, thus leaving q − e substrings without error).

Mapping assembly

The above follows immediately from the pigeon-hole principleand is known as the filtering or partitioning into exactmatches strategy.

Mapping assembly

The q − e substrings that exactly match the genomeconstitute an anchor.

Mapping assembly

There exist( q

possible anchor combinations of the qfragments of a read that we have to check and also extend.

Mapping assembly

There exist( q

possible anchor combinations of the qfragments of a read that we have to check and also extend.

In practice, for the seed part, we use q = 4 and e = 2:( q

= 6 combinations.

Mapping assembly

Mapping assembly algorithms: BWT

Burrows-Wheeler Transform (BWT) (Burrows and Wheeler,1994) is an algorithm used in data compression applicationssuch as bzip2.

Mapping assembly

It can be applied to create a permanent index of the referencesequence, which may be re-used across mapping runs.

Mapping assembly

Consider the n × n matrix in which each row contains adifferent cyclic rotation of the original text of length n.

Mapping assembly

Consider the n × n matrix in which each row contains adifferent cyclic rotation of the original text of length n.Sort the rows lexicographically.

Mapping assembly

Consider the n × n matrix in which each row contains adifferent cyclic rotation of the original text of length n.Sort the rows lexicographically.BWT is the rightmost column in the sorted matrix.

Mapping assembly

If the text has several repeating substrings, then the BWT willhave several places where a single character is repeated;e.g. BWT(mississippi) = pssmipissii.

Mapping assembly

If the text has several repeating substrings, then the BWT willhave several places where a single character is repeated;e.g. BWT(mississippi) = pssmipissii.

The remarkable thing about the BWT is that it isreversible—allowing the original text to be re-generated onlyfrom the last column!

Mapping assembly

mississippi

imississipp

pimississip

ppimississi

ippimississ

sippimissis

ssippimissi

issippimiss

sissippimis

ssissippimi

ississippim

Table: n × n matrix of the cyclic rotations of mississippi

Mapping assembly

Prefix of n − 1 letters nth letter (BWT)

imississip p

ippimissis s

issippimis s

ississippi m

mississipp i

pimississi p

ppimississ i

sippimissi s

sissippimi s

ssippimiss i

ssissippim i

Table: n × n lexicographically sorted matrix of the cyclic rotations ofmississippi

Mapping assembly

There exists a direct relationship between the BWT and thesuffix array—an efficient indexing data structure from whichwe may obtain directly the BWT.

Mapping assembly

The amount of storage that we need to store the BWT,however, is significantly smaller than that suffix array.

Mapping assembly

An increasing number of algorithms is developed to searchthese compressed full-text indexes for permitting fastsubstring queries; the most well-known is the FM-index(Ferragina and Manzini, 2000).

Mapping assembly

It can be used to efficiently find the number of occurrences ofa pattern within the compressed text, as well as to locate theposition of each occurrence.

Mapping assembly

Both the query time and storage space requirements aresublinear with respect to the size of the input data.

Mapping assembly

Both the query time and storage space requirements aresublinear with respect to the size of the input data.

Most recent mapping tools are based on such BWT indexes.

Mapping assembly

Mapping assembly algorithms: some experiments

Table: Mapping 25, 000, 000 64 bp-long simulated reads to the humanchromosome 6 (166, 880, 988 bp)

Programme Total time Reads alignedIndexing Mapping

SOAP2 5m10s 28m25s 22,699,605REAL -q 0 0m00s 26m43s 22,509,708Bowtie 7m35s 49m11s 21,594,916REAL -q 1 0m00s 31m54s 22,519,739

All programmes were run with 48 bp-long seed, with at most two

mismatches in the seed, and reported best hits only.

Mapping assembly

Table: Mapping 24, 543, 488 70 bp-long simulated reads to theDrosophila melanogaster chromosome 3L (24, 543, 557 bp)

Programme Total time Reads aligned AccuracyIndexing Mapping

SOAP2 0m45s 16m02s 21,126,303 99,98%REAL -q 0 0m00s 10m44s 21,134,692 99,98%Bowtie 0m59s 40m28s 18,920,716 96,09%REAL -q 1 0m00s 15m42s 21,134,699 99,98%All programmes were run with 48 bp-long seed, with at most two

mismatches in the seed, and reported the best hits only.

Mapping assembly

Table: Mapping 24, 163, 065 76 bp-long real reads to the human genome

Programme Total time Reads alignedIndexing Mapping

SOAP2 1h58m07s 1h52m21s 12,664,760REAL -q 0 0m00s 4h08m47s 11,813,271Bowtie 3h29m59s 1h56m41s 10,789,260REAL -q 1 0m00s 4h20m37s 11,738,732

All programmes were run with 48 bp-long seed, with at most two

mismatches in the seed, and reported the best hits only.

Contents

1 Introduction

2 Basic definitions

3 Alignment algorithms on strings

4 Conclusion

Overview

BLAST: a set of programs for the comparison of biologicalsequences.

Overview

Recent technological advances have dramatically improvednext-generation sequencing throughput and quality.

Overview

In parallel with the technological improvements that haveincreased the throughput of the next-generation short-readsequencers, many algorithmic advances have been made.

Overview

De novo assembly: assembling short reads to createfull-length—sometimes novel—sequences.

Overview

De novo assembly: assembling short reads to createfull-length—sometimes novel—sequences.

Mapping assembly: assembling reads by aligning them againstan existing reference sequence—building a sequence that issimilar but not necessarily identical to the reference.

Overview

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Overview

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Overview

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Overview

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M. Margulies, M. Egholm, W. E. Altman, S. Attiya, J. S.Bader, L. A. Bemben, J. Berka, M. S. Braverman, Y.-J. Chen,Z. Chen, S. B. Dewell, L. Du, J. M. Fierro, X. V. Gomes, B. C.Godwin, W. He, S. Helgesen, C. H. Ho, G. P. Irzyk, S. C.Jando, M. L. I. Alenquer, T. P. Jarvie, K. B. Jirage, J.-B. Kim,J. R. Knight, J. R. Lanza, J. H. Leamon, S. M. Lefkowitz,M. Lei, J. Li, K. L. Lohman, H. Lu, V. B. Makhijani, K. E.McDade, M. P. McKenna, E. W. Myers, E. Nickerson, J. R.Nobile, R. Plant, B. P. Puc, M. T. Ronan, G. T. Roth, G. J.Sarkis, J. F. Simons, J. W. Simpson, M. Srinivasan, K. R.Tartaro, A. Tomasz, K. A. Vogt, G. A. Volkmer, S. H. Wang,

Overview

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Overview

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& Catering Future Blast Chiller and Blast Chiller/Freezer … · 2017-08-17 · Future Blast Chiller and Blast Chiller/Freezer ... models with integral air cooled ... BC/BF Blast

Utilization of high throughput genome sequencing …...2015/09/23 · Mega BLAST 2.2.26 (Zhang et al. 2000) was used to map the deer reads to the cattle reference genome UMD 3.0 with

Lecture 4 BLAST & Genome assembly - HITS gGmbHsco.h-its.org/exelixis/web/teaching/lectures14_15/lecture4.pdf · – By-reference assembly Hash indexes Burrows-Wheeler ... De novo

Point Specific Alignment Methods PSI – BLAST & PHI – BLAST

Introduction to Nephrology - Jennifer Smith's Professional …jennifersmithprofessionalportfol.weebly.co… · PPT file · Web view · 2014-02-15Introduction to Nephrology. Jennifer