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> Procedures > Molecular Biology Research Technique > Sequence Alignment

Sequence Alignment

Sequence Alignmnet is a fundamental bioinformatics technique used to identify regions of similarity between biological sequences, such as DNA, RNA, or protein sequences.
This process allows researchers to uncover evolutionary relationships, predict protein structure and function, and design effective research protocols.
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Its intelligent comparisons enable you to identify the optimal protocols and products for your project, saving you time and improving your research outcomes.
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Most cited protocols related to «Sequence Alignment»

1
Simulating 16S Sequence Alignments
Cited 3990 times
The simulated protein alignments and the genuine COG alignments were described previously [2] (link). The 16S alignment with 237,882 distinct sequences was taken from GreenGenes [33] (link) (http://greengenes.lbl.gov). The 16S alignment with 15,011 distinct “families” is a non-redundant subset of these sequences ( identical). 16S alignments with 500 sequences are also non-redundant random subsets ( identical). Other large 16S alignments are from [11] (link).
For the 16S-like simulations with 78,132 distinct sequences, we used a maximum-likelihood tree inferred from a non-redundant aligned subset of the full set of 16S sequences ( % identity) by an earlier version of FastTree (1.9) with the Jukes-Cantor model (no CAT). To ensure that the simulated trees were resolvable, which facilitates comparison of methods (but inflates the accuracy of all methods), branch lengths of less than 0.001 were replaced with values of 0.001, which corresponds to roughly one substitution across the internal branch, as the 16S alignment has 1,287 positions. Evolutionary rates for each site were randomly selected from 16 rate categories according to a gamma distribution with a coefficient of variation of 0.7. Given the tree and the rates, sequences were simulated with Rose [34] (link) under the HKY model and no transition bias. To allow Rose to handle branch lengths of less than 1%, we set “MeanSubstitution = 0.00134” and multiplied the branch lengths by 1,000.
Price M.N., Dehal P.S, & Arkin A.P. (2010). FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments. PLoS ONE, 5(3), e9490.
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Publication 2010
Biological Evolution Cantor Gamma Rays Proteins Sequence Alignment Trees
2
BEDTools: Versatile Genomic Data Exploration
Cited 3605 times
Table 1 illustrates the wide range of operations that BEDTools support. Many of the tools have extensive parameters that allow user-defined overlap criteria and fine control over how results are reported. Importantly, we have also defined a concise format (BEDPE) to facilitate comparisons of discontinuous features (e.g. paired-end sequence reads) to each other (pairToPair), and to genomic features in traditional BED format (pairToBed). This functionality is crucial for interpreting genomic rearrangements detected by paired-end mapping, and for identifying fusion genes or alternative splicing patterns by RNA-seq. To facilitate comparisons with data produced by current DNA sequencing technologies, intersectBed and pairToBed compute overlaps between sequence alignments in BAM format (Li et al., 2009 (link)), and a general purpose tool is provided to convert BAM alignments to BED format, thus facilitating the use of BAM alignments with all other BEDTools (Table 1). The following examples illustrate the use of intersectBed to isolate single nucleotide polymorphisms (SNPs) that overlap with genes, pairToBed to create a BAM file containing only those alignments that overlap with exons and intersectBed coupled with samtools to create a SAM file of alignments that do not intersect (-v) with repeats.

Summary of supported operations available in the BEDTools suite

UtilityDescription
intersectBed*Returns overlaps between two BED files.
pairToBedReturns overlaps between a BEDPE file and a BED file.
bamToBedConverts BAM alignments to BED or BEDPE format.
pairToPairReturns overlaps between two BEDPE files.
windowBedReturns overlaps between two BED files within a user-defined window.
closestBedReturns the closest feature to each entry in a BED file.
subtractBed*Removes the portion of an interval that is overlapped by another feature.
mergeBed*Merges overlapping features into a single feature.
coverageBed*Summarizes the depth and breadth of coverage of features in one BED file relative to another.
genomeCoverageBedHistogram or a ‘per base’ report of genome coverage.
fastaFromBedCreates FASTA sequences from BED intervals.
maskFastaFromBedMasks a FASTA file based upon BED coordinates.
shuffleBedPermutes the locations of features within a genome.
slopBedAdjusts features by a requested number of base pairs.
sortBedSorts BED files in useful ways.
linksBedCreates HTML links from a BED file.
complementBed*Returns intervals not spanned by features in a BED file.

Utilities in bold support sequence alignments in BAM. Utilities with an asterisk were compared with Galaxy and found to yield identical results.

Other notable tools include coverageBed, which calculates the depth and breadth of genomic coverage of one feature set (e.g. mapped sequence reads) relative to another; shuffleBed, which permutes the genomic positions of BED features to allow calculations of statistical enrichment; mergeBed, which combines overlapping features; and utilities that search for nearby yet non-overlapping features (closestBed and windowBed). BEDTools also includes utilities for extracting and masking FASTA sequences (Pearson and Lipman, 1988 (link)) based upon BED intervals. Tools with similar functionality to those provided by Galaxy were directly compared for correctness using the ‘knownGene’ and ‘RepeatMasker’ tracks from the hg19 build of the human genome. The results from all analogous tools were found to be identical (Table 1).
Quinlan A.R, & Hall I.M. (2010). BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6), 841-842.
Publication 2010
Exons Gene Fusion Gene Rearrangement Genes Genome Genome, Human Sequence Alignment Single Nucleotide Polymorphism
3
Efficient Genomic Data Visualization with Multiresolution Formats
Cited 2941 times
To support the multiresolution data model described earlier, we developed a corresponding file format. The ‘tiled data format’, or TDF, stores the pyramidal data tile structure and provides fast access to individual tiles. TDF files can be created using the auxiliary package ‘igvtools’. We note however that IGV does not require conversion to TDF before data can be loaded. In fact, IGV supports a variety of genomic file formats, which can be divided into three categories: (i) nonindexed, (ii) indexed and (iii) multiresolution formats:

Nonindexed formats include flat file formats such as GFF [11 ], BED [12 ] and WIG [13 ]. Files in these formats must be read in their entirety and are only suitable for relatively small data sets.

Indexed formats include BAM and Goby [14 ] for sequence alignments. Additionally, many tab-delimited feature formats can be converted to an indexed file using Tabix [15 (link)] or ‘igvtools’. Indexed formats provide rapid and efficient access to subsets of the data for display, but only when zoomed in to a sufficiently small genomic region. Zooming out requires ever-larger portions of the file to be loaded. Thus, indexed formats can efficiently support views only for a limited range of resolution scales. This range depends on the genomic density of the underlying data and can span tens of kilobases for NGS alignments, hundreds of megabases for typical variant (SNP) files, or whole chromosomes for sparse feature files. IGV uses heuristics to determine a suitable upper limit on the genomic range that can be loaded quickly with a reasonable memory footprint. If zoomed out beyond this limit, the data are not loaded.

Multiresolution formats, such as our TDF described earlier and the bigWig and bigBed formats [16 (link)], include both an index for the raw data, and precomputed indexed summary data for lower resolution (zoomed out) scales. Multiresolution formats can efficiently support views at any resolution scale.

Thorvaldsdóttir H., Robinson J.T, & Mesirov J.P. (2012). Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Briefings in Bioinformatics, 14(2), 178-192.
Publication 2012
Chromosomes Genome Memory Sequence Alignment Toxic Epidermal Necrolysis
4
Accelerating CD-HIT Clustering Algorithm
Cited 2615 times
Basically, CD-HIT is a greedy incremental algorithm that starts with the longest input sequence as the first cluster representative, and then process the remaining sequences from long to short to classify each sequence as a redundant or representative sequence based on its similarities to the existing representatives. The similarities are estimated by common word counting using word indexing and counting tables to filter out unnecessary sequence alignments, which are used to compute exact similarities. In the following sections, we will describe the techniques that are used to accelerate CD-HIT.
Fu L., Niu B., Zhu Z., Wu S, & Li W. (2012). CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics, 28(23), 3150-3152.
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Publication 2012
Sequence Alignment
5
Comparative Sequence Analysis Software Benchmarking
Cited 1960 times
Software versions used: SAM 3.5 (Jul 2005) [37] (link), NCBI BLAST+ 2.2.24+ (Aug 2010) [3] (link), FASTA 36.3.3 (Feb 2011) [40] , WU-BLAST 2.0MP-WashU (May 2006), HMMER 2.3.2 (Oct 2003), and HMMER 3.0 (Mar 2010).
Example sequence alignments and profile HMMs were sampled from Seed alignments and profiles in Pfam 24 [11] (link). Example target sequences were sampled from UniProt version 2011_03 [43] (link). One experiment that characterized roundoff error used older versions, Pfam 22 and UniProt 7.0.
, & Eddy S.R. (2011). Accelerated Profile HMM Searches. PLoS Computational Biology, 7(10), e1002195.
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Publication 2011
FCER2 protein, human Hypertelorism, Severe, With Midface Prominence, Myopia, Mental Retardation, And Bone Fragility Sequence Alignment

Most recents protocols related to «Sequence Alignment»

1
Comparative Analysis of TT2 and MYB5 in Brassica
The TT2 and MYB5 protein sequences of the six Brassica species and Arabidopsis were used to generate phylogenetic trees via ClustalX [26 (link)] and MAFFT sofaware (Katoh and Standley, 2013) multiple sequence alignments with the default parameters. A maximum likelihood (ML) phylogenetic tree was constructed using FastTree2 software (v2.1.11), in which JTT (Jones-Taylor-Thornton) model was the best substitution model [52 (link)]. The TT2 and MYB5 promoter regions of 2000 bp regions upstream of the translational start sites ATG were examined based on their positions in the genomes of six Brassica species and Arabidopsis using Samtools software (v 1.8), which was used to identify the cis-elements in the promoters according to the online PlantCARE database (http://bioinformatics.psb.ugent.be/webtools/plantcare/html/). The gene structures of TT2 and MYB5 were analyzed according to the GFF annotation file of the gene position information in the six Brassica crops and Arabidopsis database. The MEME online tool (https://meme-suite.org/meme/) was used to investigate conserved domains, and the WEBLoGo online tool (https://weblogo.berkeley.edu/) and SWISS-MODEL online tool (https://swissmodel.expasy.org/) was used to draw spatial structure. TBtools software (v0.67) was used to draw the TT2 and MYB5 to the different copies of each Brassica species, including phylogenetic, promoter characteristics, gene structure, conserved motifs [4 ].
Chen D., Chen H., Dai G., Zhang H., Liu Y., Shen W., Zhu B., Cui C, & Tan C. (2023). Genome-wide identification and expression analysis of the anthocyanin-related genes during seed coat development in six Brassica species. BMC Genomics, 24, 103.
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Publication 2023
Amino Acid Sequence Arabidopsis Brassica Crop, Avian Gene Order Genes Genetic Structures Genome Protein Biosynthesis Sequence Alignment
2
Phylogenetic Analysis of N-Metabolic Genes
N-metabolic genes were selected, and alignment of these genes was performed. The top three similar gene sequences of nitrate assimilation and denitrification were retrieved after doing BLASTP against the NCBI Nr database for the sequence alignment. Sequence Manipulation Suite version 2 was used for alignment and polished the protein sequences [20 (link)]. All the protein sequences of N-metabolism genes (assimilatory and respiratory nitrate reductase, nitrite reductase, nitric oxide reductase, hydroxylamine reductase, and glutamine synthetase) of Lelliottia amnigena and their similarities genes were analyzed by BLASTP and saved in FASTA format as an input file. To investigate the phylogenetic relationship of selected nitrogen metabolism genes was performed with the help of the MEGA 11(Mega Evolutionary Genetic Analysis version 11) tool. First, the protein sequence was aligned with MUSCLE and phylogenetic tree was constructed based on neighbor-joining [21 (link)]. The percentage of bootstrap [22 (link)] values were shown at the nodes. The evolutionary distances were computed using the Jones Taylor Thornton method [23 (link)] and are in the units of the number of amino acids substitutions per site. Branch length are given below the node. It defines the genetic changes i.e., longer the branch more genetic changes.
Thakur P, & Gauba P. (2023). Identification and examination of nitrogen metabolic genes in Lelliottia amnigena PTJIIT1005 for their ability to perform nitrate remediation. BMC Genomics, 24, 104.
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Publication 2023
Amino Acid Sequence Amino Acid Substitution Biological Evolution Denitrification Genes Glutamate-Ammonia Ligase hydroxylamine reductase Lelliottia amnigena MEGA 11 Metabolism Muscle Tissue Nitrate Reductase Nitrates nitric oxide reductase Nitrite Reductase Nitrogen nucleoprotein, Measles virus Reproduction Sequence Alignment
3
Chloroplast genome diversity in Seriphidium
Sequence alignment of the 20 subg. Seriphidium samples complete chloroplast genomes was conducted using MAFFT v. 7 [77 (link)]. The Mauve v. 2.3.1 [78 (link)], with default parameters, was used to identify locally collinear blocks among the chloroplast genomes. The genome variability across the 20 subg. Seriphidium samples was assessed using mVISTA [79 (link)] in Shuffle-LAGAN mode. Expansions and contractions of inverted repeat regions were visualized at the junctions of the four main (LSC/IRb/SSC/IRa) of the chloroplast genome, via IRScope [80 (link)]. Nucleotide diversity (Pi) was estimated by sliding window analysis conducted in DnaSP v. 6 [81 (link)] (window length, 600 bp; step size, 200 bp).
Jin G., Li W., Song F., Yang L., Wen Z, & Feng Y. (2023). Comparative analysis of complete Artemisia subgenus Seriphidium (Asteraceae: Anthemideae) chloroplast genomes: insights into structural divergence and phylogenetic relationships. BMC Plant Biology, 23, 136.
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Publication 2023
Genome Genome, Chloroplast Nucleotides Sequence Alignment
4
Structural Characterization of Staphylococcal Adhesins

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Publication 2023
Base Sequence Cloning Vectors Exons Figs Hexosaminidase A Histidine Lectin Recombination, Genetic Sequence Alignment
5
Lung mononuclear cells miRNA analysis
miRNA analysis was carried out as described previously (Alharris et al., 2018 (link); Neamah et al., 2019 (link)). Briefly, total RNA, including miRNA, was isolated from lung mononuclear cells using the miRNeasy kit from QIAGEN and following the protocol of the company. Microarray was performed using Affymetrix miRNA Array (version 4.1). Raw files generated from the miRNA microarray were uploaded to Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) and deposited under accession number GSE220159. By using Transcriptome Analysis Console (TAC, ThermoFisher, United States), Log2 fold change of more than 3,000 miRNAs was detected from the raw array data, and only those miRNAs that were altered more than 2-fold were considered for further analysis. Filtered miRNAs were analyzed for their role in various biological pathways using Ingenuity Pathway Analysis (IPA) software http://www.ingenuity.com (Qiagen, Germany). Also, microRNA.org database was used to examine the sequence alignment regions between miR-100-5p and its targeted genes.
Alghetaa H., Mohammed A., Singh N., Wilson K., Cai G., Putluri N., Nagarkatti M, & Nagarkatti P. (2023). Resveratrol attenuates staphylococcal enterotoxin B-activated immune cell metabolism via upregulation of miR-100 and suppression of mTOR signaling pathway. Frontiers in Pharmacology, 14, 1106733.
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Publication 2023
Biopharmaceuticals Cells Gene Expression Gene Expression Profiling Genes Lung Microarray Analysis MicroRNAs Sequence Alignment

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1
Bigdye terminator v3.1 cycle sequencing kit by Thermo Fisher Scientific
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The BigDye Terminator v3.1 Cycle Sequencing Kit is a reagent kit used for DNA sequencing. It contains the necessary components, including fluorescently labeled dideoxynucleotides, to perform the Sanger sequencing method.
2
Hiseq 2000 by Illumina
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The HiSeq 2000 is a high-throughput DNA sequencing system designed by Illumina. It utilizes sequencing-by-synthesis technology to generate large volumes of sequence data. The HiSeq 2000 is capable of producing up to 600 gigabases of sequence data per run.
3
Hiseq 2500 by Illumina
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The HiSeq 2500 is a high-throughput DNA sequencing system designed for a wide range of applications, including whole-genome sequencing, targeted sequencing, and transcriptome analysis. The system utilizes Illumina's proprietary sequencing-by-synthesis technology to generate high-quality sequencing data with speed and accuracy.
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The QIAquick Gel Extraction Kit is a product designed for the purification of DNA fragments from agarose gels. It efficiently extracts and purifies DNA from gel slices after electrophoresis.
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Miseq platform by Illumina
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The MiSeq platform is a benchtop sequencing system designed for targeted, amplicon-based sequencing applications. The system uses Illumina's proprietary sequencing-by-synthesis technology to generate sequencing data. The MiSeq platform is capable of generating up to 15 gigabases of sequencing data per run.
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Pmd18 t vector by Takara Bio
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The PMD18-T vector is a plasmid used for cloning and maintaining DNA sequences in Escherichia coli. It contains a multiple cloning site for inserting target DNA, an ampicillin resistance gene for selection, and a pUC origin of replication for high-copy number propagation in bacteria.
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Trizol reagent by Thermo Fisher Scientific
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TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.
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Dnaman software by Lynnon Biosoft
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DNAMAN is a software tool for sequence analysis and manipulation. It provides basic functions for DNA/RNA/protein sequence viewing, editing, and management.
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Pymol molecular graphics system by Schrödinger
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PyMOL is a molecular visualization software package for rendering and animating 3D molecular structures. It allows users to display, analyze, and manipulate molecular models, providing a powerful tool for research in fields such as biochemistry, structural biology, and drug design.

More about "Sequence Alignment"

Sequence alignment is a fundamental bioinformatics technique used to identify regions of similarity between biological sequences, such as DNA, RNA, or protein sequences.
This process allows researchers to uncover evolutionary relationships, predict protein structure and function, and design effective research protocols.
Sequence alignment is commonly performed using various software tools and algorithms, including the BigDye Terminator v3.1 Cycle Sequencing Kit, HiSeq 2000, HiSeq 2500, and MiSeq platforms.
The QIAquick PCR Purification Kit and QIAquick Gel Extraction Kit are often used in conjunction with sequence alignment to purify and extract DNA samples for analysis.
The PMD18-T vector and TRIzol reagent can also be employed in sample preparation for sequence alignment.
DNAMAN software and the PyMOL Molecular Graphics System are commonly used to visualize and analyze the results of sequence alignment, allowing researchers to identify evolutionary relationships, predict protein structure, and design effective research protocols.
Sequence alignment is a crucial step in bioinformatics and genomics research, enabling scientists to uncover valuable insights and drive scientific discoveries.
By utilizing the latest tools and techniques, researchers can streamline the sequence alignment process and improve their research outcomes, as exemplified by the AI-driven platform PubCompare.ai, which helps locate the best research protocols from literature, pre-prints, and patents, saving time and enhancing research efficacy.