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Sequence Analysis

Sequence Analysis is a powerful tool for understanding the structure and function of biological sequences, such as DNA, RNA, and proteins.
This AI-driven process involves examining the order of nucleotides or amino acids within a sequence to identify patterns, similarities, and differences.
Researchers can use Sequence Analysis to locate the most effective research protocols from literature, preprints, and patents, optimizing their studies and uncovering the most efficient products.
With intelligent analysis, Sequence Analysis helps expereience the future of sequence research, providing a concise, informative overview of this essential bioinformatics technique.

Most cited protocols related to «Sequence Analysis»

1
Comprehensive Overrepresented Sequence Analysis
Some sequences, or even entire reads, can be overrepresented in FASTQ data. Analysis of these overrepresented sequences provides an overview of certain sequencing artifacts such as PCR over-duplication, polyG tails and adapter contamination. FASTQC offers an overrepresented sequence analysis module, however, according to the author’s introduction, FASTQC only tracks the first 1 M reads of the input file to conserve memory. We suggest that inferring the overall distribution from the first 1 M reads is not a reliable solution as the initial reads in Illumina FASTQ data usually originate from the edges of flowcell lanes, which may have lower quality and different patterns than the overall distribution.
Unlike FASTQC, fastp samples all reads evenly to evaluate overrepresented sequences and eliminate partial distribution bias. To achieve an efficient implementation of this feature, we designed a two-step method. In the first step, fastp completely analyzes the first 1.5 M base pairs of the input FASTQ to obtain a list of sequences with relatively high occurrence frequency in different sizes. In the second step, fastp samples the entire file and counts the occurrence of each sequence. Finally, the sequences with high occurrence frequency are reported.
Besides the occurrence frequency, fastp also records the positions of overrepresented sequences. This information is quite useful for diagnosing sequence quality issues. Some sequences tend to appear in the read head whereas others appear more often in the read tail. The distribution of overrepresented sequences is visualized in the HTML report. Figure 5 shows a demonstration of overrepresented sequence analysis results.
Chen S., Zhou Y., Chen Y, & Gu J. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 34(17), i884-i890.
Publication 2018
Head Memory Poly G Sequence Analysis Tail
2
Bayesian Phylogenetic Inference with Relaxed Clocks
The full Bayesian sequence analysis with an uncorrelated relaxed-clock model allows the co-estimation of substitution parameters, relaxed-clock parameters, and the ancestral phylogeny. The posterior distribution is of the following form:

The vector Φ contains the parameters of the relaxed-clock model (e.g., μ and σ
2 in the case of lognormally distributed rates among branches). The term Pr
D|g,Φ,Ω is the standard Felsenstein likelihood, where
g is a tree with branch length measured in units of time. For the purposes of calculating this likelihood, branch lengths are converted to units of substitutions by multiplying the rates defined by Φ with the internode distance between node
i and parent node
j in tree
g. The tree prior,
f
G(
g|Θ), can either be a coalescent-based prior [
30 (link),
65 (link)] for within-population data or some other appropriate prior if the sequences come from multiple populations/species [
55 (link)]. The vector Θ contains the hyperparameters of the tree prior. The vector Ω contains the parameters of the substitution model (such as transition/transversion ratio, κ; shape parameter for gamma-distributed rates among sites, α; and proportion of invariant sites,
p
inv).
We summarize the posterior density in
Equation 5 using samples (
g,Θ,Φ,Ω) ∼
f obtained via MCMC. If, for example, the divergence times are of primary interest then the other sampled parameters can be thought of as nuisance parameters, and vice versa.
The formulation in
Equation 5 implies that the branch-rates could be integrated analytically in the Felsenstein likelihood. Although this could be accomplished relatively easily by discretizing the rate distribution and averaging the likelihood over the rate categories on each branch, we elected to do the integration using MCMC. This was achieved by assigning a unique rate category
c ∈ 1,2,…,2
n−2 to each branch
j of the tree. During the calculation of the likelihood the rate category
c is converted to a rate by the following method:

The function
D−1(
x) is the inverse function of the probability distribution function,
D(
x)=
P(
X≤
x), of the relaxed-clock model specified by Equations
2 and
3. This discretization of the underlying rate distribution is illustrated in
Figure 5 for a lognormal distribution with 12 rate categories (sufficient for a tree of seven tips). To integrate the branch rates out, the assignment of rate categories
c to branches was sampled via MCMC.
Drummond A.J., Ho S.Y., Phillips M.J, & Rambaut A. (2006). Relaxed Phylogenetics and Dating with Confidence. PLoS Biology, 4(5), e88.
Publication 2006
Cloning Vectors Gamma Rays Parent Population Group Sequence Analysis Trees
3
Technical Artifacts in Transcript Quantification
The analyses presented in Fig. 1d, Supplementary Table 1 and Supplementary Fig. 5 were carried out on a subset of 30 samples from the publicly-available GEUVADIS [11 (link)] data. The accessions used and the information about the center at which the libraries were prepared and sequenced is recorded in Supplementary Table 3. All methods were run with bias correction enabled, using a transcriptome built with the RefSeq gene annotation file and the genome FASTA contained within the hg19 Illumina iGenome, to allow for comparison with the results in [5 ].
For each transcript, a t-test was performed, comparing log2 (TPM + 1) from 15 samples from one sequencing center against 15 samples from another sequencing center. P values were then adjusted using the method of Benjamini-Hochberg, over the transcripts with mean TPM > 0.1. The number of positives for given false discovery rates was then reported for each method, by taking the number of transcripts with adjusted p value less than a given threshold.
Because the samples are from the same human population, it is expected that there would be few to no true differences in transcript abundance produced by this comparison. This assumption was confirmed by permuting the samples and performing t-tests as well as making t-test comparisons of random subsets within sequencing center, which consistently produced ≪ 1 DE transcript on average for all methods. Such an analysis comparing samples across sequencing center was specifically chosen to highlight transcripts with false quantification differences arising from technical artifacts.
Patro R., Duggal G., Love M.I., Irizarry R.A, & Kingsford C. (2017). Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference. Nature methods, 14(4), 417-419.
Publication 2017
Gene Annotation Genome Homo sapiens Sequence Analysis Transcriptome
4
NGS QC Toolkit: Advancing Sequencing Data Analysis
First version (v1.0) of the NGS QC toolkit included QC tools (IlluQC.pl and 454QC.pl) with basic functionality of quality check and primer/adaptor contamination removal for Illumina and Roche 454 data generating textual QC statistics, and sequence statistics analysis tools. In a major update of the toolkit (v2.0), parallelization was introduced in the QC tools to speed up the analysis. In addition, the feature of generating QC statistics in the form of graphs was implemented. We have also added the feature of reading/writing of compressed files (gzip) and generating consolidated QC report in HTML format in our earlier update (v2.1). Recently, IlluQC tools were updated to generate a graph depicting percentage of reads falling into different quality score ranges at each base position, TrimmingReads tool was modified to provide an additional option for trimming reads based on quality score and a new tool has been incorporated for the QC of Roche 454 paired-end data in the current version (v2.2).
Patel R.K, & Jain M. (2012). NGS QC Toolkit: A Toolkit for Quality Control of Next Generation Sequencing Data. PLoS ONE, 7(2), e30619.
Publication 2012
Oligonucleotide Primers Sequence Analysis
5
Comparative Genomic Analysis Across Bacterial Pathogens
Analysis of the full set of PMEN1 sequences used the alignment from (26 (link)); 11 closely-related isolates were extracted as a subsample for comparison with the output of ClonalFrame. For the analysis of S. aureus ST239, 14 representatives from the South-East Asian clade were extracted from the larger alignment (49 (link)) for the equivalent comparative analysis. For the analysis of Helicobacter pylori, eight publically available complete genomes were selected from across the species that included both the most closely-related pair of isolates and the isolate most divergent from the rest of the sample, based on a previous analysis (50 (link)). These genomes were then aligned using progressiveMauve (51 (link)), generating a 1.8 Mb core genome alignment for analysis.
The resulting whole genome alignments were then analyzed using the default settings of Gubbins, except that the S. pneumoniae and S. aureus analyses were run until convergence. For S. pneumoniae and S. aureus, ClonalFrame (19 (link)) was also run using default settings, without estimating node ages, with a burn in chain length of 25 000 and a parameter estimation chain length of 25 000. For H. pylori, convergence was achieved when ClonalFrame was run without estimating node ages or theta, using a burn in chain length of 10 000 and a parameter estimation chain length of 10 000. Convergence was assessed through plotting the variation in parameter values over the course of the MCMC; these are shown in Supplementary Figures S4, S7 and S9.
Croucher N.J., Page A.J., Connor T.R., Delaney A.J., Keane J.A., Bentley S.D., Parkhill J, & Harris S.R. (2014). Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Research, 43(3), e15.
Publication 2014
Genome Helicobacter pylori Sequence Analysis Southeast Asian People Staphylococcus aureus Streptococcus pneumoniae

Most recents protocols related to «Sequence Analysis»

1
Micropeptides Identified from lncRNAs
Not available on PMC !

Example 1

The authors of the invention have identified 3 micropeptides corresponding to sequences SEQ ID NO: 1, 2 and 3.

The micropeptide of SEQ ID NO 1 is a highly conserved 87 aa micropeptide whose sequence is:

(FIG. 1A)
MEGLRRGLSRWKRYHIKVHLADEALLLPLTVRPRDTLSDLRAQLVGQGVSS
WKRAFYYNARRLDDHQTVRDARLQDGSVLLLVSDPR.

In silico analysis of the amino acid sequence predicts a 3D structure resembling the protein UBIQUITIN (FIG. 1B). SEQ ID NO 1 micropeptide is coded by the lncRNA TINCR (LINC00036 in humans and Gm20219 in mice).

The micropeptide of SEQ ID NO: 2 is a 64-amino acid micropeptide whose sequence is:

(FIG. 2A)
MVRRKSMKKPRSVGEKKVEAKKQLPEQTVQKPRQECREAGPLFLQSRRETR
DPETRATYLCGEG.

It is encoded by ZEB2 antisense 1 (ZEB2AS1) long non-coding RNA (lncRNA). ZEB2AS1 is a natural antisense transcript corresponding to the 5′ untranslated region (UTR) of zinc finger E-box binding homeobox 2 (ZEB2). The ORF encoding the micropeptide spams part of the second and third exons of the lncRNA. I-Tasser, a 3D protein structure predictor, has been used in order to build a model of SEQ ID NO: 2 micropeptide 3D structure (FIG. 2B). Further in-silico analysis has revealed high amino acidic sequence conservation across the species and a potential cytoplasmatic localization of the micropeptide of SEQ ID NO: 2.

The micropeptide of SEQ ID NO: 3 is a 78-amino acid micropeptide encoded by the first exon of LINC0086 lncRNA. Its sequence, highly conserved across evolution is:

(FIG. 3A)
MAASAALSAAAAAAALSGLAVRLSRSAAARGSYGAFCKGLTRTLLTFFDLA
WRLRMNFPYFYIVASVMLNVRLQVRIE.

In silico analysis of this sequence predicted a tertiary structure (FIG. 3B) with a transmembrane domain at C-terminal of the protein and a signal peptide in the first 25 amino acids.

US11858972B2. Micropeptides and uses thereof (2024-01-02). FUNDACIÓ PRIVADA INSTITUT D'INVESTIGACIÓ ONCOLÒGICA DE VALL HEBRON [ES]. Inventors: Maria Abad Méndez [ES], Olga Boix Sánchez [ES], Emanuela Greco [ES], Iñaki Merino Valverde [ES].
Patent 2024
Amino Acids Amino Acid Sequence Biological Evolution Cytoplasm Exons Homo sapiens Integral Membrane Proteins Mice, House Protein Domain Proteins RNA, Long Untranslated Sequence Analysis Sequence Analysis, Protein Signal Peptides Ubiquitin Zinc Finger E-box Binding Homeobox 2
2
Sequence Alignment and Identity Analysis
Not available on PMC !

Example 3

Alignment of SEQ ID NO: 1 to SEQ ID NO: 100 was performed using the software Align X, a component of Vector NTI Advanced 11.5.4 by Invitrogen. Several groups of sequences have at least 90%, at least 70%, or at least 50% nucleotide sequence identity as illustrated in the alignments of FIGS. 13, 14, and 15. In these alignments, only the central variable region of the aptamers was included for simplicity. Thus, oligonucleotides with at least 50%, at least 70%, or at least 90% nucleotide sequence identity to sequences selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 200 are included as part of the current invention.

US11859187B2. Aptamers for personal health care applications (2024-01-02). The Procter & Gamble Company [US]. Inventors: Juan Esteban Velasquez [US], Spencer Christopher Rupard [US], Amy Violet Trejo [US], Adam Michael Pitz [US], Kelly Lee Schmeichel [US], Erin Nicole Swigart [US], Gregory Allen Penner [CA].
Patent 2024
Cloning Vectors Figs Oligonucleotides Sequence Analysis
3
Preparation and Purification of cDNA
Not available on PMC !

Example 3

PCR procedures for the preparation of cDNA may be performed using 2×KAPA HIFI™ HotStart ReadyMix by Kapa Biosystems (Woburn, MA). This system includes 2×KAPA ReadyMix 12.5 μl; Forward Primer (10 μM) 0.75 μl; Reverse Primer (10 μM) 0.75 μl; Template cDNA 100 ng; and dH2O diluted to 25.0 μl. The reaction conditions may be at 95° C. for 5 min. The reaction may be performed for 25 cycles of 98° C. for 20 sec, then 58° C. for 15 sec, then 72° C. for 45 sec, then 72° C. for 5 min, then 4° C. to termination.

The reaction may be cleaned up using Invitrogen's PURELINK™ PCR Micro Kit (Carlsbad, CA) per manufacturer's instructions (up to 5 μg). Larger reactions may require a cleanup using a product with a larger capacity. Following the cleanup, the cDNA may be quantified using the NANODROP™ and analyzed by agarose gel electrophoresis to confirm that the cDNA is the expected size. The cDNA may then be submitted for sequencing analysis before proceeding to the in vitro transcription reaction.

US11872278B2. Combination HMPV/RSV RNA vaccines (2024-01-16). ModernaTX, Inc. [US]. Inventors: Giuseppe Ciaramella [US], Sunny Himansu [US].
Patent 2024
Adjustment Disorders DNA, Complementary Electrophoresis, Agar Gel Oligonucleotide Primers Sequence Analysis Transcription, Genetic
4
Serological Evidence of Canine Influenza Virus Circulation
Not available on PMC !

Example 5

To investigate whether a Canine/FL/04-like influenza virus had circulated among greyhound populations in Florida prior to the January 2004 outbreak, archival sera from 65 racing greyhounds were tested for the presence of antibodies to Canine/FL/04 using the HI and MN assays. There were no detectable antibodies in 33 dogs sampled from 1996 to 1999. Of 32 dogs sampled between 2000 and 2003, 9 were seropositive in both assays—1 in 2000, 2 in 2002, and 6 in 2003 (Table 5). The seropositive dogs were located at Florida tracks involved in outbreaks of respiratory disease of unknown etiology from 1999 to 2003, suggesting that a Canine/FL/04-like virus may have been the causative agent of those outbreaks. To investigate this possibility further, we examined archival tissues from greyhounds that died from hemorrhagic bronchopneumonia in March 2003. Lung homogenates inoculated into MDCK cells and chicken embryos from one dog yielded H3N8 influenza virus, termed A/Canine/Florida/242/2003 (Canine/FL/03). Sequence analysis of the complete genome of Canine/FL/03 revealed >99% identity to Canine/FL/04 (Table 4), indicating that Canine/FL/04-like viruses had infected greyhounds prior to 2004.

US11865172B2. Materials and methods for respiratory disease control in canines (2024-01-09). UNIVERSITY OF FLORIDA RESEARCH FOUNDATION, INC. [US], CORNELL RESEARCH FOUNDATION, INC. [US], The Government of The United States of America as represented by The Secretary of the Department of [US]. Inventors: Patti Cynthia Crawford [US], Paul J. Gibbs [US], Edward J. Dubovi [US], Ruben Omar Donis [US], Jacqueline Katz [US], Alexander I. Klimov [US], Nallakannu P. Lakshmanan [US], Melissa Anne Lum [US], Daniel Ghislena Emiel Goovaerts [NL], Mark William Mellencamp [US], Nancy J. Cox [US], William L. Castleman [US].
Patent 2024
Antibodies Biological Assay Bronchopneumonia Canis familiaris Chickens Disease Outbreaks Embryo Genome Hemorrhage Influenza Influenza A Virus, H3N8 Subtype Lung Madin Darby Canine Kidney Cells Orthomyxoviridae Population Group Respiration Disorders Respiratory Rate Sequence Analysis Serum Tissues Virus
5
Reducing C9orf72 Transcripts via CRISPR-Cas9
Not available on PMC !

Example 2

In this example, guide RNAs were designed to target exon 3 after the ATG initiation codon of C9orf72 (Table 2). The strategy was to introduce small indels that will lead to early termination codon, thus inducing non-sense mediated decay of C9orf72 transcripts to reduce RNA foci and dipeptide formation. FIG. 6A shows the human C9orf72 gene sequence of exon 3 with the locations of the non-sense mediated decay (NMD) guide RNA 1r and 2f and the location and sequence of PCR indel analysis primers C9NMD Indel F1 and R1 marked. FIG. 6B shows the results of agarose gel electrophoresis of the PCR products amplified by the C9NMD-Indel F1 and R1 PCR primers. In this example, HEK293T cells were transfected with LV-SpCas9 (Control) or LV-NMDgR-SpCas9 plasmid (2 μg) in triplicate. FIG. 6C shows the results of digital droplet PCT (ddPCR) analysis of the C9orf72 RNA levels from FIG. 6B.

TABLE 2
Guide RNAs generated for
“Non-sense mediated decay.”
SEQ
ID
guide RNAguide RNA sequenceNO:
NMD gRNA 1rUCGAAAUGCAGAGAGUGGUG5
NMD gRNA 2fAAUGGGGAUCGCAGCACAUA6

US11859179B2. Methods of treating amyotrophic lateral sclerosis (ALS) (2024-01-02). University of Massachusetts [US]. Inventors: Christian Mueller [US], Abbas Abdallah [US].
Patent 2024
Cells Codon, Initiator Codon, Terminator Dipeptides Electrophoresis, Agar Gel Exons Fingers INDEL Mutation Oligonucleotide Primers Plasmids RNA RNA Decay RNA Sequence Sequence Analysis

Top products related to «Sequence Analysis»

1
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.
2
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.
3
Rneasy mini kit by Qiagen
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The RNeasy Mini Kit is a laboratory equipment designed for the purification of total RNA from a variety of sample types, including animal cells, tissues, and other biological materials. The kit utilizes a silica-based membrane technology to selectively bind and isolate RNA molecules, allowing for efficient extraction and recovery of high-quality RNA.
4
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.
5
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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Qiaquick pcr purification kit by Qiagen
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The QIAquick PCR Purification Kit is a lab equipment product designed for the rapid purification of PCR (Polymerase Chain Reaction) amplicons. It utilizes a silica-membrane technology to efficiently capture and purify DNA fragments from PCR reactions, removing unwanted primers, nucleotides, and enzymes.
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Pgem t easy vector by Promega
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The pGEM-T Easy Vector is a high-copy-number plasmid designed for cloning and sequencing of PCR products. It provides a simple, efficient method for the insertion and analysis of PCR amplified DNA fragments.
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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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Agilent 2100 bioanalyzer by Agilent Technologies
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The Agilent 2100 Bioanalyzer is a lab instrument that provides automated analysis of DNA, RNA, and protein samples. It uses microfluidic technology to separate and detect these biomolecules with high sensitivity and resolution.
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Trizol by Thermo Fisher Scientific
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TRIzol is a monophasic solution of phenol and guanidine isothiocyanate that is used for the isolation of total RNA from various biological samples. It is a reagent designed to facilitate the disruption of cells and the subsequent isolation of RNA.

More about "Sequence Analysis"

Sequence analysis is a powerful bioinformatics technique that involves examining the order of nucleotides or amino acids within biological sequences, such as DNA, RNA, and proteins.
This process, driven by advanced AI algorithms, helps researchers identify patterns, similarities, and differences within these sequences.
Leveraging sequence analysis, scientists can locate the most effective research protocols from a vast array of literature, preprints, and patents, optimizing their studies and uncovering the most efficient products.
Key subtopics in sequence analysis include nucleotide and amino acid sequence alignment, phylogenetic analysis, and secondary structure prediction.
Commonly used tools and kits in sequence analysis include the TRIzol reagent for RNA extraction, the BigDye Terminator v3.1 Cycle Sequencing Kit for DNA sequencing, the RNeasy Mini Kit for purifying RNA, and the QIAquick PCR Purification Kit for cleaning up PCR products.
High-throughput sequencing platforms like the HiSeq 2000, HiSeq 2500, and MiSeq are also integral to sequence analysis, providing the necessary data for in-depth investigations.
Sequence analysis helps researchers expereience the future of sequence research, providing a concise and informative overview of this essential bioinformatics technique.
By harnessing the power of AI-driven analysis, scientists can uncover valuable insights, optimize their research protocols, and stay at the forefront of sequence-based discoveries.