HamHeat: A fast and simple package for calculating Hamming distance from multiple sequence data for heatmap visualization

AbstractThe problem of fast calculation of Hamming distance inferred from many sequence datasets is still not a trivial task. Here, we present HamHeat, as a new software package to efficiently calculate Hamming distance for hundreds of aligned protein or DNA sequences of a large number of residues or nucleotides, respectively. HamHeat uses a unique algorithm with many advantages, including its ease of use and the execution of fast runs for large amounts of data. The package consists of three consecutive modules. In the first module, the software ranks the sequences from the most to the least frequent variant. The second module uses the most common variant as the reference sequence to calculate the Hamming distance of each additional sequence based on the number of residue or nucleotide changes. A final module formats all the results in a comprehensive table that displays the sequence ranks and Hamming distances.Availability and implementationHamHeat is based on Python 3 and AWK, runs on Linux system and is available under the MIT License at: https://github.com/alexeyrakov/[email protected]

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Provide a secured automatic program algorithm for DNA sequence storage arrangement in cloud

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.20.11733 ◽

2018 ◽

Vol 7 (2.20) ◽

pp. 10

Author(s):

G Geetha ◽

G Surekha ◽

P Aditya Sharma ◽

E Uma Shankari

Keyword(s):

Cloud Computing ◽

Dna Sequences ◽

Sequence Data ◽

Low Cost ◽

Original Data ◽

Reference Sequence ◽

Secret Data ◽

Security Issues ◽

Information Accessibility ◽

Minimum Number

The primary target of this paper is to provide a secured implementing algorithm for hiding DNA sample sequence data confidently by using special software in cloud computing environments. The suggested algorithm here for hiding DNA sequences is based on binary coding and complementary pairing rules. Hence DNA reference sequence is taken as a sample secret data with a notation of M. But after applying some steps the final result obtained in cloud environment is M’’’. The procedure of identifying or extracting the original data M from the hidden DNA Reference sequence is depended on the user if and only if the user wants to use the data for process. Likewise there are security issues for the manipulating from claiming information. In this way that accessible user’s information arrangement may be isolated under SPs in such a way that it has to reach minimum number of specified threshold SPs number from the whole data block. In this paper, we recommend A low cost secured and multi- cloud storage (SCMCS) model over cloud computing which holds an prudent appropriation about information Around the accessible SPs in the market, with gatherings gives client information accessibility and additionally secure capacity.

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Statistical studies of biomolecular sequences: score-based methods

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.1994.0078 ◽

1994 ◽

Vol 344 (1310) ◽

pp. 391-402 ◽

Cited By ~ 13

Keyword(s):

Dna Sequences ◽

Protein Sequence ◽

Sequence Data ◽

Genome Composition ◽

Sequence Comparisons ◽

Multiple Sequence ◽

Challenges And Opportunities ◽

Protein Sequence Data ◽

Statistical Studies ◽

Massive Accumulation

The massive accumulation of DNA and protein sequence data poses challenges and opportunities in terms of interpretation and analysis. This presentation reviews the method of score-based sequence analysis with the objectives of discerning distinctive segments in single sequences and identifying significant common segments in sequence comparisons. A number of new results are described here for both the theory and its applications. These include distributional theory involving several high scoring segments in single sequences, distribution formulas for general scoring regimes in multiple sequence comparisons, bounds for periodic scoring assignments, sensitivity analysis of genome composition and refinements on predicting exons and genes in DNA sequences.

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Culturing Putatively Obligate Epizoic Diatoms: Insights for the Evolution and Ecology of Diatoms and Their Host

10.21203/rs.3.rs-1041030/v1 ◽

2021 ◽

Author(s):

Matt Ashworth ◽

Roksana Majewska ◽

Thomas A Frankovich ◽

Michael Sullivan ◽

Sunčica Bosak ◽

...

Keyword(s):

Dna Sequences ◽

Cultured Cells ◽

Sequence Data ◽

Environmental Dna ◽

Sea Turtle ◽

Reference Sequence ◽

Generalist Species ◽

In The Wild ◽

Host Ecology ◽

Marine Megafauna

Abstract Background: Our understanding of the importance of microbiomes on large aquatic animals—such as whales, sea turtles and manatees—has advanced considerably in recent years. Recent activity describing the epizoic diatoms growing on marine vertebrates suggests that these epibiotic diatom communities constitute diverse, polyphyletic, and compositionally stable assemblages that include both putatively obligate epizoic and generalist species. Here, we outline a successful attempt to culture putatively obligate epizoic diatoms without their hosts and propose further applications and research avenues in this growing area of study. Results: We cultured cells of epizoic diatoms from multiple host species sampled in the wild and captivity. Analyzing the DNA sequences of these cultures, we found that several unique diatom taxa have independently evolved to occupy in epibiotic habitats. We created a library of reference sequence data for use in metabarcoding surveys of sea turtle and manatee microbiomes that will further facilitate the use of environmental DNA for studying host specificity in epizoic diatoms and the utility of diatoms as indicators of host ecology and health. Conclusions: Our discovery that epizoic diatoms can be cultured independently from their hosts raises several questions about the nature of the interaction between these diatom species and their hosts. We encourage the interdisciplinary community working with marine megafauna to consider including diatom sampling and diatom analysis into their routine practices.

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Quantifying uncertainty of taxonomic placement in DNA barcoding and metabarcoding

10.1101/070573 ◽

2016 ◽

Cited By ~ 3

Author(s):

Panu Somervuo ◽

Douglas Yu ◽

Charles Xu ◽

Yinqiu Ji ◽

Jenni Hultman ◽

...

Keyword(s):

Dna Barcoding ◽

Dna Sequences ◽

Sequence Data ◽

Environmental Data ◽

Reference Sequence ◽

Future Research ◽

List Type ◽

Reference Databases ◽

Sequence Databases ◽

Taxonomic Groups

AbstractA crucial step in the use of DNA markers for biodiversity surveys is the assignment of Linnaean taxonomies (species, genus, etc.) to sequence reads. This allows the use of all the information known based on the taxonomic names. Taxonomic placement of DNA barcoding sequences is inherently probabilistic because DNA sequences contain errors, because there is natural variation among sequences within a species, and because reference databases are incomplete and can have false annotations. However, most existing bioinformatics methods for taxonomic placement either exclude uncertainty, or quantify it using metrics other than probability.In this paper we evaluate the performance of a recently proposed probabilistic taxonomic placement method PROTAX by applying it to both annotated reference sequence data as well as unknown environmental data. Our four case studies include contrasting taxonomic groups (fungi, bacteria, mammals, and insects), variation in the length and quality of the barcoding sequences (from individually Sanger-sequenced sequences to short Illumina reads), variation in the structures and sizes of the taxonomies (from 800 to 130 000 species), and variation in the completeness of the reference databases (representing 15% to 100% of the species).Our results demonstrate that PROTAX yields essentially unbiased assessment of probabilities of taxonomic placement, and thus that its quantification of species identification uncertainty is reliable. As expected, the accuracy of taxonomic placement increases with increasing coverage of taxonomic and reference sequence databases, and with increasing ratio of genetic variation among taxonomic levels over within taxonomic levels.Our results show that reliable species-level identification from environmental samples is still challenging, and thus neglecting identification uncertainty can lead to spurious inference. A key aim for future research is the completion and pruning of taxonomic and reference sequence databases, and making these two types of data compatible.

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Deoxyribonucleic Acid as a Tool for Digital Information Storage: An Overview

THE INDIAN JOURNAL OF VETERINARY SCIENCES AND BIOTECHNOLOGY ◽

10.21887/ijvsbt.15.1.1 ◽

2019 ◽

Vol 15 (01) ◽

pp. 1-8

Author(s):

Ashish C Patel ◽

C G Joshi

Keyword(s):

Data Storage ◽

Dna Sequences ◽

Consensus Sequence ◽

Random Access ◽

Information Storage ◽

Digital Data ◽

Digital Information ◽

Multiple Sequence ◽

Digital World ◽

Digital File

Current data storage technologies cannot keep pace longer with exponentially growing amounts of data through the extensive use of social networking photos and media, etc. The "digital world” with 4.4 zettabytes in 2013 has predicted it to reach 44 zettabytes by 2020. From the past 30 years, scientists and researchers have been trying to develop a robust way of storing data on a medium which is dense and ever-lasting and found DNA as the most promising storage medium. Unlike existing storage devices, DNA requires no maintenance, except the need to store at a cool and dark place. DNA has a small size with high density; just 1 gram of dry DNA can store about 455 exabytes of data. DNA stores the informations using four bases, viz., A, T, G, and C, while CDs, hard disks and other devices stores the information using 0’s and 1’s on the spiral tracks. In the DNA based storage, after binarization of digital file into the binary codes, encoding and decoding are important steps in DNA based storage system. Once the digital file is encoded, the next step is to synthesize arbitrary single-strand DNA sequences and that can be stored in the deep freeze until use.When there is a need for information to be recovered, it can be done using DNA sequencing. New generation sequencing (NGS) capable of producing sequences with very high throughput at a much lower cost about less than 0.1 USD for one MB of data than the first sequencing technologies. Post-sequencing processing includes alignment of all reads using multiple sequence alignment (MSA) algorithms to obtain different consensus sequences. The consensus sequence is decoded as the reversal of the encoding process. Most prior DNA data storage efforts sequenced and decoded the entire amount of stored digital information with no random access, but nowadays it has become possible to extract selective files (e.g., retrieving only required image from a collection) from a DNA pool using PCR-based random access. Various scientists successfully stored up to 110 zettabytes data in one gram of DNA. In the future, with an efficient encoding, error corrections, cheaper DNA synthesis,and sequencing, DNA based storage will become a practical solution for storage of exponentially growing digital data.

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DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome

Bioinformatics ◽

10.1093/bioinformatics/btab083 ◽

2021 ◽

Author(s):

Yanrong Ji ◽

Zhihan Zhou ◽

Han Liu ◽

Ramana V Davuluri

Keyword(s):

Dna Sequences ◽

Regulatory Elements ◽

Ease Of Use ◽

Fine Tuning ◽

Supplementary Information ◽

Sequence Motifs ◽

Semantic Relationship ◽

Accurate Identification ◽

Conserved Sequence ◽

Genome Wide

Abstract Motivation Deciphering the language of non-coding DNA is one of the fundamental problems in genome research. Gene regulatory code is highly complex due to the existence of polysemy and distant semantic relationship, which previous informatics methods often fail to capture especially in data-scarce scenarios. Results To address this challenge, we developed a novel pre-trained bidirectional encoder representation, named DNABERT, to capture global and transferrable understanding of genomic DNA sequences based on up and downstream nucleotide contexts. We compared DNABERT to the most widely used programs for genome-wide regulatory elements prediction and demonstrate its ease of use, accuracy and efficiency. We show that the single pre-trained transformers model can simultaneously achieve state-of-the-art performance on prediction of promoters, splice sites and transcription factor binding sites, after easy fine-tuning using small task-specific labeled data. Further, DNABERT enables direct visualization of nucleotide-level importance and semantic relationship within input sequences for better interpretability and accurate identification of conserved sequence motifs and functional genetic variant candidates. Finally, we demonstrate that pre-trained DNABERT with human genome can even be readily applied to other organisms with exceptional performance. We anticipate that the pre-trained DNABERT model can be fined tuned to many other sequence analyses tasks. Availability and implementation The source code, pretrained and finetuned model for DNABERT are available at GitHub (https://github.com/jerryji1993/DNABERT). Supplementary information Supplementary data are available at Bioinformatics online.

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Characterization of squalene synthase gene from Gymnema sylvestre R. Br.

Beni-Suef University Journal of Basic and Applied Sciences ◽

10.1186/s43088-020-00094-4 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Kuldeepsingh A. Kalariya ◽

Ram Prasnna Meena ◽

Lipi Poojara ◽

Deepa Shahi ◽

Sandip Patel

Keyword(s):

Dna Sequences ◽

Genomic Dna ◽

Competitive Inhibition ◽

Sequence Data ◽

Homology Model ◽

Squalene Synthase ◽

Gymnema Sylvestre ◽

Gardenia Jasminoides ◽

Ramachandran Plots ◽

Flanking Regions

Abstract Background Squalene synthase (SQS) is a rate-limiting enzyme necessary to produce pentacyclic triterpenes in plants. It is an important enzyme producing squalene molecules required to run steroidal and triterpenoid biosynthesis pathways working in competitive inhibition mode. Reports are available on information pertaining to SQS gene in several plants, but detailed information on SQS gene in Gymnema sylvestre R. Br. is not available. G. sylvestre is a priceless rare vine of central eco-region known for its medicinally important triterpenoids. Our work aims to characterize the GS-SQS gene in this high-value medicinal plant. Results Coding DNA sequences (CDS) with 1245 bp length representing GS-SQS gene predicted from transcriptome data in G. sylvestre was used for further characterization. The SWISS protein structure modeled for the GS-SQS amino acid sequence data had MolProbity Score of 1.44 and the Clash Score 3.86. The quality estimates and statistical score of Ramachandran plots analysis indicated that the homology model was reliable. For full-length amplification of the gene, primers designed from flanking regions of CDS encoding GS-SQS were used to get amplification against genomic DNA as template which resulted in approximately 6.2-kb sized single-band product. The sequencing of this product through NGS was carried out generating 2.32 Gb data and 3347 number of scaffolds with N50 value of 457 bp. These scaffolds were compared to identify similarity with other SQS genes as well as the GS-SQSs of the transcriptome. Scaffold_3347 representing the GS-SQS gene harbored two introns of 101 and 164 bp size. Both these intronic regions were validated by primers designed from adjoining outside regions of the introns on the scaffold representing GS-SQS gene. The amplification took place when the template was genomic DNA and failed when the template was cDNA confirmed the presence of two introns in GS-SQS gene in Gymnema sylvestre R. Br. Conclusion This study shows GS-SQS gene was very closely related to Coffea arabica and Gardenia jasminoides and this gene harbored two introns of 101 and 164 bp size.

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Deep Neural Networks Classification via Binary Error-Detecting Output Codes

Applied Sciences ◽

10.3390/app11083563 ◽

2021 ◽

Vol 11 (8) ◽

pp. 3563

Author(s):

Martin Klimo ◽

Peter Lukáč ◽

Peter Tarábek

Keyword(s):

Neural Networks ◽

Categorical Data ◽

Coding Theory ◽

Hamming Distance ◽

Activation Function ◽

Block Codes ◽

Ease Of Use ◽

Linear Block Codes ◽

Binary Coding ◽

The One

One-hot encoding is the prevalent method used in neural networks to represent multi-class categorical data. Its success stems from its ease of use and interpretability as a probability distribution when accompanied by a softmax activation function. However, one-hot encoding leads to very high dimensional vector representations when the categorical data’s cardinality is high. The Hamming distance in one-hot encoding is equal to two from the coding theory perspective, which does not allow detection or error-correcting capabilities. Binary coding provides more possibilities for encoding categorical data into the output codes, which mitigates the limitations of the one-hot encoding mentioned above. We propose a novel method based on Zadeh fuzzy logic to train binary output codes holistically. We study linear block codes for their possibility of separating class information from the checksum part of the codeword, showing their ability not only to detect recognition errors by calculating non-zero syndrome, but also to evaluate the truth-value of the decision. Experimental results show that the proposed approach achieves similar results as one-hot encoding with a softmax function in terms of accuracy, reliability, and out-of-distribution performance. It suggests a good foundation for future applications, mainly classification tasks with a high number of classes.

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Toward a global reference database of COI barcodes for marine zooplankton

Marine Biology ◽

10.1007/s00227-021-03887-y ◽

2021 ◽

Vol 168 (6) ◽

Author(s):

Ann Bucklin ◽

Katja T. C. A. Peijnenburg ◽

Ksenia N. Kosobokova ◽

Todd D. O’Brien ◽

Leocadio Blanco-Bercial ◽

...

Keyword(s):

Species Diversity ◽

Dna Sequences ◽

Reference Sequence ◽

Global Ocean ◽

Reference Database ◽

Data Repositories ◽

Marine Zooplankton ◽

The North ◽

Coi Sequences ◽

Taxonomic Groups

AbstractCharacterization of species diversity of zooplankton is key to understanding, assessing, and predicting the function and future of pelagic ecosystems throughout the global ocean. The marine zooplankton assemblage, including only metazoans, is highly diverse and taxonomically complex, with an estimated ~28,000 species of 41 major taxonomic groups. This review provides a comprehensive summary of DNA sequences for the barcode region of mitochondrial cytochrome oxidase I (COI) for identified specimens. The foundation of this summary is the MetaZooGene Barcode Atlas and Database (MZGdb), a new open-access data and metadata portal that is linked to NCBI GenBank and BOLD data repositories. The MZGdb provides enhanced quality control and tools for assembling COI reference sequence databases that are specific to selected taxonomic groups and/or ocean regions, with associated metadata (e.g., collection georeferencing, verification of species identification, molecular protocols), and tools for statistical analysis, mapping, and visualization. To date, over 150,000 COI sequences for ~ 5600 described species of marine metazoan plankton (including holo- and meroplankton) are available via the MZGdb portal. This review uses the MZGdb as a resource for summaries of COI barcode data and metadata for important taxonomic groups of marine zooplankton and selected regions, including the North Atlantic, Arctic, North Pacific, and Southern Oceans. The MZGdb is designed to provide a foundation for analysis of species diversity of marine zooplankton based on DNA barcoding and metabarcoding for assessment of marine ecosystems and rapid detection of the impacts of climate change.

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Using next generation sequencing of alpine plants to improve fecal metabarcoding diet analysis for Dall’s sheep

BMC Research Notes ◽

10.1186/s13104-021-05590-z ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Kelly E. Williams ◽

Damian M. Menning ◽

Eric J. Wald ◽

Sandra L. Talbot ◽

Kumi L. Rattenbury ◽

...

Keyword(s):

Sequence Data ◽

Vascular Plant ◽

Alpine Plants ◽

Diet Analysis ◽

Reference Sequence ◽

Reference Library ◽

Ovis Dalli ◽

Plant Animal Interactions ◽

Dall’S Sheep ◽

Northwestern North America

Abstract Objectives Dall’s sheep (Ovis dalli dalli) are important herbivores in the mountainous ecosystems of northwestern North America, and recent declines in some populations have sparked concern. Our aim was to improve capabilities for fecal metabarcoding diet analysis of Dall’s sheep and other herbivores by contributing new sequence data for arctic and alpine plants. This expanded reference library will provide critical reference sequence data that will facilitate metabarcoding diet analysis of Dall’s sheep and thus improve understanding of plant-animal interactions in a region undergoing rapid climate change. Data description We provide sequences for the chloroplast rbcL gene of 16 arctic-alpine vascular plant species that are known to comprise the diet of Dall’s sheep. These sequences contribute to a growing reference library that can be used in diet studies of arctic herbivores.

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