Computational Molecular Biology Research Articles

LISTER: Semiautomatic Metadata Extraction from Annotated Experiment Documentation in eLabFTW.

The availability of scientific methods, code, and data is key for reproducing an experiment. Research data should be made available following the FAIR principle (findable, accessible, interoperable, and reusable). For that, the annotation of research data with metadata is central. However, existing research data management workflows often require that metadata be created by the corresponding researchers, which takes effort and time. Here, we developed LISTER as a methodological and algorithmic solution to create and extract metadata from annotated, template-based experimental documentation using minimum effort. We focused on tailoring the integration between existing platforms by using eLabFTW as the electronic lab notebook and adopting the ISA (investigation, study, assay) model as the abstract data model framework. LISTER consists of four components: annotation language to support metadata extraction; customized eLabFTW entries using specific hierarchies, templates, and tags to structure reusable scientific documentation; a "container" concept in eLabFTW, making metadata of a particular container content extractable along with its underlying, related experiments via a single click; a Python-based app to enable easy-to-use, semiautomated metadata extraction from eLabFTW entries. LISTER outputs metadata in machine-readable .json and human-readable .xlsx formats, and Material and Methods (MM) descriptions in .docx format that could be used in a thesis or manuscript. The metadata can be used as a basis to create or extend ontologies, which, when applied to the published research data, will significantly enhance its value. DSpace is used as a data cataloging platform for hosting the extracted metadata and research data. We applied LISTER to computational biophysical chemistry, protein biochemistry, and molecular biology, and our concept should be extendable to other life science areas.

Quantum gate algorithm for reference-guided DNA sequence alignment.

Reference-guided DNA sequencing and alignment is an important process in computational molecular biology. The amount of DNA data grows very fast, and many new genomes are waiting to be sequenced while millions of private genomes need to be re-sequenced. Each human genome has 3.2B base pairs, and each one could be stored with 2 bits of information, so one human genome would take 6.4B bits or ∼760MB of storage (National Institute of General Medical Sciences, n.d.). Today's most powerful tensor processing units cannot handle the volume of DNA data necessitating a major leap in computing power. It is, therefore, important to investigate the usefulness of quantum computers in genomic data analysis, especially in DNA sequence alignment. Quantum computers are expected to be involved in DNA sequencing, initially as parts of classical systems, acting as quantum accelerators. The number of available qubits is increasing annually, and future quantum computers could conduct DNA sequencing, taking the place of classical computing systems. We present a novel quantum algorithm for reference-guided DNA sequence alignment modeled with gate-based quantum computing. The algorithm is scalable, can be integrated into existing classical DNA sequencing systems and is intentionally structured to limit computational errors. The quantum algorithm has been tested using the quantum processing units and simulators provided by IBM Quantum, and its correctness has been confirmed.

Chaining for accurate alignment of erroneous long reads to acyclic variation graphs.

Aligning reads to a variation graph is a standard task in pangenomics, with downstream applications such as improving variant calling. While the vg toolkit [Garrison et al. (Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat Biotechnol 2018;36:875-9)] is a popular aligner of short reads, GraphAligner [Rautiainen and Marschall (GraphAligner: rapid and versatile sequence-to-graph alignment. Genome Biol 2020;21:253-28)] is the state-of-the-art aligner of erroneous long reads. GraphAligner works by finding candidate read occurrences based on individually extending the best seeds of the read in the variation graph. However, a more principled approach recognized in the community is to co-linearly chain multiple seeds. We present a new algorithm to co-linearly chain a set of seeds in a string labeled acyclic graph, together with the first efficient implementation of such a co-linear chaining algorithm into a new aligner of erroneous long reads to acyclic variation graphs, GraphChainer. We run experiments aligning real and simulated PacBio CLR reads with average error rates 15% and 5%. Compared to GraphAligner, GraphChainer aligns 12-17% more reads, and 21-28% more total read length, on real PacBio CLR reads from human chromosomes 1, 22, and the whole human pangenome. On both simulated and real data, GraphChainer aligns between 95% and 99% of all reads, and of total read length. We also show that minigraph [Li et al. (The design and construction of reference pangenome graphs with minigraph. Genome Biol 2020;21:265-19.)] and minichain [Chandra and Jain (Sequence to graph alignment using gap-sensitive co-linear chaining. In: Proceedings of the 27th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2023). Springer, 2023, 58-73.)] obtain an accuracy of <60% on this setting. GraphChainer is freely available at https://github.com/algbio/GraphChainer. The datasets and evaluation pipeline can be reached from the previous address.

DNA Image Encryption Scheme Based on a Chaotic LSTM Pseudo-Random Number Generator

Deoxyribonucleic Acid (DNA) coding technology is a new research field developed by the combination of computer science and molecular biology, that has been gradually applied in the field of image encryption in recent years. Furthermore, sensitivity to initial conditions, pseudo-random properties, and state ergodicity of coupled chaotic maps can help produce good pseudo-random number generators and meet the requirements of an image encryption system well. In this paper, an image encryption algorithm based on high-dimensional coupled chaotic maps and DNA coding is proposed. A pseudo-random sequence is generated by a long short-term memory (LSTM) architecture using the proposed maps and evaluated through a set of statistical tests to show the high performance of the proposed generator. All intensity values of an input image are converted to a binary sequence, which is scrambled globally by the high-dimensional coupled chaotic maps. The DNA operations are performed on the scrambled binary sequences instead of binary operations to increase the algorithm efficiency. Simulation results and performance analyses demonstrate that the proposed encryption scheme is extremely sensitive to small changes in secret keys, provides high security and can resist differential attack.

Analyzing Histone Chaperone ANTI-SILENCING FACTOR-1A and HISTONE REPRESSOR A Interactions and Their Roles in Arabidopsis thaliana Development via CRISPR/Cas9 Mutagenesis

The ASF1A and HIRA are histone chaperones highly conserved in many organisms. The protein interactions between ASF1a and HIRA and their role in plant development are not understood in Arabidopsis thaliana. Here, we present our attempt to generate mutations at ASF1a and HIRA genes using CRISPR/Cas9 technology and analyze their interactions through computational molecular biology. Our current efforts focus on preliminary sequence analyses, CRISPR/Cas9 system preparation, and transgene delivery.

LambdaPP: Fast and accessible protein‐specific phenotype predictions

The availability of accurate and fast artificial intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first internet server making AI protein predictions available in 1992. Given a protein sequence as input, LambdaPP provides easily accessible visualizations of protein 3D structure, along with predictions at the protein level (GeneOntology, subcellular location), and the residue level (binding to metal ions, small molecules, and nucleotides; conservation; intrinsic disorder; secondary structure; alpha-helical and beta-barrel transmembrane segments; signal-peptides; variant effect) in seconds. The structure prediction provided by LambdaPP-leveraging ColabFold and computed in minutes-is based on MMseqs2 multiple sequence alignments. All other feature prediction methods are based on the pLM ProtT5. Queried by a protein sequence, LambdaPP computes protein and residue predictions almost instantly for various phenotypes, including 3D structure and aspects of protein function. LambdaPP is freely available for everyone to use under embed.predictprotein.org, the interactive results for the case study can be found under https://embed.predictprotein.org/o/Q9NZC2. The frontend of LambdaPP can be found on GitHub (github.com/sacdallago/embed.predictprotein.org), and can be freely used and distributed under the academic free use license (AFL-2). For high-throughput applications, all methods can be executed locally via the bio-embeddings (bioembeddings.com) python package, or docker image at ghcr.io/bioembeddings/bio_embeddings, which also includes the backend of LambdaPP.

Markov field models: Scaling molecular kinetics approaches to large molecular machines

With recent advances in structural biology, including experimental techniques and deep learning-enabled high-precision structure predictions, molecular dynamics methods that scale up to large biomolecular systems are required. Current state-of-the-art approaches in molecular dynamics modeling focus on encoding global configurations of molecular systems as distinct states. This paradigm commands us to map out all possible structures and sample transitions between them, a task that becomes impossible for large-scale systems such as biomolecular complexes. To arrive at scalable molecular models, we suggest moving away from global state descriptions to a set of coupled models that each describe the dynamics of local domains or sites of the molecular system. We describe limitations in the current state-of-the-art global-state Markovian modeling approaches and then introduce Markov field models as an umbrella term that includes models from various scientific communities, including Independent Markov decomposition, Ising and Potts models, and (dynamic) graphical models, and evaluate their use for computational molecular biology. Finally, we give a few examples of early adoptions of these ideas for modeling molecular kinetics and thermodynamics.

BOA: A partitioned view of genome assembly

SummaryDe novo genome assembly is a fundamental problem in computational molecular biology that aims to reconstruct an unknown genome sequence from a set of short DNA sequences (or reads) obtained from the genome. The relative ordering of the reads along the target genome is not known a priori, which is one of the main contributors to the increased complexity of the assembly process. In this article, with the dual objective of improving assembly quality and exposing a high degree of parallelism, we present a partitioning-based approach. Our framework, BOA (bucket-order-assemble), uses a bucketing alongside graph- and hypergraph-based partitioning techniques to produce a partial ordering of the reads. This partial ordering enables us to divide the read set into disjoint blocks that can be independently assembled in parallel using any state-of-the-art serial assembler of choice. Experimental results show that BOA improves both the overall assembly quality and performance.

Michael Waterman's Contributions to Computational Biology and Bioinformatics

On the occasion of Dr. Michael Waterman's 80th birthday, we review his major contributions to the field of computational biology and bioinformatics including the famous Smith-Waterman algorithm for sequence alignment, the probability and statistics theory related to sequence alignment, algorithms for sequence assembly, the Lander-Waterman model for genome physical mapping, combinatorics and predictions of ribonucleic acid structures, word counting statistics in molecular sequences, alignment-free sequence comparison, and algorithms for haplotype block partition and tagSNP selection related to the International HapMap Project. His books Introduction to Computational Biology: Maps, Sequences and Genomes for graduate students and Computational Genome Analysis: An Introduction geared toward undergraduate students played key roles in computational biology and bioinformatics education. We also highlight his efforts of building the computational biology and bioinformatics community as the founding editor of the Journal of Computational Biology and a founding member of the International Conference on Research in Computational Molecular Biology (RECOMB).

Protein Structure Prediction Using Quantile Dragonfly and Structural Class-Based Deep Learning

Predicting three-dimensional structure of a protein in the field of computational molecular biology has received greater attention. Most of the recent research works aimed at exploring search space, however with the increasing nature and size of data, protein structure identification and prediction are still in the preliminary stage. This work is aimed at exploring search space to tackle protein structure prediction with minimum execution time and maximum accuracy by means of quantile regressive dragonfly and structural class homolog-based deep learning (QRD-SCHDL). The proposed QRD-SCHDL method consists of two distinct steps. They are protein structure identification and prediction. In the first step, protein structure identification is performed by means of QRD optimization model to identify protein structure with minimum error. Here the protein structure identification is first performed as the raw database contains sequence information and does not contain structural information. An optimization model is designed to obtain the structural information from the database. However, protein structure gives much more insight than its sequence. Therefore, to perform computational prediction of protein structure from its sequence, actual protein structure prediction is made. The second step involves the actual protein structure prediction via structural class and homolog-based deep learning. For each protein structure prediction, a scoring matrix is obtained by utilizing structural class maximum correlation coefficient. Finally, the proposed method is tested on a set of different unique numbers of protein data and compared to the state-of-the-art methods. The obtained results showed the potentiality of the proposed method in terms of metrics, error rate, protein structure prediction time, protein structure prediction accuracy, precision, specificity, recall, ROC, Kappa coefficient and [Formula: see text]-measure, respectively. It also shows that the proposed QRD-SCHDL method attains comparable results and outperformed in certain cases, thereby signifying the efficiency of the proposed work.

Protein secondary structure prediction using a lightweight convolutional network and label distribution aware margin loss

Protein secondary structure prediction (PSSP) is an important task in computational molecular biology. Recently, deep neural networks have demonstrated great potential in improving the performance of eight-class PSSP. However, the existing deep predictors usually have higher model complexity and ignore the class imbalance of eight-class secondary structure data in training. In addition, the current methods cannot guarantee that the features corresponding to the padded residue positions are always zero during the forward propagation of the network, which will cause the prediction results of the same protein chain to be different under varied zero-padding numbers. To this end, we propose a novel lightweight convolutional network ShuffleNet_SS, which adopts modified 1-dimensional batch normalization to eliminate the impact of padded residue positions on nonpadded residue positions and uses the label distribution aware margin loss to enhance the network’s ability to learn rare classes. In particular, in order to enable ShuffleNet_SS to fully achieve cross-group information exchange, we further improve the standard channel shuffle operation. Experimental results on the benchmark datasets including CASP10, CASP11, CASP12, CASP13, CASP14 and CB513 show that the proposed method achieves state-of-the-art performance with much lower parameters compared to the five existing deep predictors. • This is the first attempt to develop a lightweight convolutional network for PSSP. • The channel shuffle is improved to fully achieve cross-group information exchange. • The class imbalance of eight-class protein secondary structure data is considered. • Our study demonstrates effectiveness of lightweight model for PSSP.

Identification of alternative protein targets of glutamate-ureido-lysine associated with PSMA tracer uptake in prostate cancer cells

Prostate-specific membrane antigen (PSMA) is highly overexpressed in most prostate cancers and is clinically visualized using PSMA-specific probes incorporating glutamate-ureido-lysine (GUL). PSMA is effectively absent from certain high-mortality, treatment-resistant subsets of prostate cancers, such as neuroendocrine prostate cancer (NEPC); however, GUL-based PSMA tracers are still reported to have the potential to identify NEPC metastatic tumors. These probes may bind unknown proteins associated with PSMA-suppressed cancers. We have identified the up-regulation of PSMA-like aminopeptidase NAALADaseL and the metabotropic glutamate receptors (mGluRs) in PSMA-suppressed prostate cancers and find that their expression levels inversely correlate with PSMA expression and are associated with GUL-based radiotracer uptake. Furthermore, we identify that NAALADaseL and mGluR expression correlates with a unique cell cycle signature. This provides an opportunity for the future study of the biology of NEPC and potential therapeutic directions. Computationally predicting that GUL-based probes bind well to these targets, we designed and synthesized a fluorescent PSMA tracer to investigate these proteins invitro, where it shows excellent affinity for PSMA, NAALADaseL, and specific mGluRs associated with poor prognosis.

An Intelligent Optimization Model for Fragment Assembly Problem

problem as it requires rebuilding the original sequence of DNA from a massive number of fragments. This paper introduces an efficient hybridization between Harris Hawks Optimization (HHO) and Problem Aware Local Search (PALS) to be utilized in solving DNA Fragment Assembly Problem (FAP). The efficiency of the proposed hybrid algor In computational molecular biology, the sequencing of Deoxyribonucleic Acid (DNA) is a very challenging ithm (PHHO) is compared with the original PALS, Firefly Algorithm (FA), Genetic Algorithm (GA), Artificial Bee Colony (ABC), and GAG50. The experimental results show the efficiency of the proposed algorithm compared to other approaches.

Improved upper bound for sorting permutations by prefix transpositions

Modelling of chromosomes with permutations has triggered the research of sorting permutations using global rearrangement operations in computational molecular biology. One such rearrangement is transposition which swaps two adjacent substrings. If one of the substrings is restricted to be the prefix of the permutation then that operation is called a prefix transposition. The symmetric prefix transposition distance between two permutations is defined to be the minimum number of prefix transpositions required to transform one into another. Group theory dictates that an upper bound for prefix transposition distance between any pair of permutations in the symmetric group S n is equivalent to the upper bound for sorting any permutation with prefix transpositions. In this paper, we improve the upper bound for sorting permutations with prefix transpositions to n − log 2 ⁡ n .

369. Alternative Workflow for SARS-CoV-2 Testing Using a Heat Lysis Protocol for Respiratory Specimens

Abstract Background The SARS-CoV-2 pandemic has demonstrated the need for streamlined workflows in high-throughput testing. In extraction-based testing, limited extraction reagents and required proprietary instrumentation may pose a bottleneck for labs. As a solution, ChromaCode developed a Direct Extraction protocol for the HDPCR™ SARS-CoV-2 Assay, distributed in accordance with the guidance on Policy for Coronavirus Disease-2019 Tests During the Public Health Emergency, Section IV.C., which allows for the processing of specimens without an extraction system. In lieu of an extraction system, the Direct Extraction protocol uses a thermal cycler to lyse and inactivate specimens which are directly added to the Polymerase Chain Reaction (PCR). Methods The Limit of Detection (LoD), Clinical Performance, and effect of Interfering Substances was determined for the Direct Extraction protocol. The LoD was established on 6 PCR platforms with dilutions of inactivated SARS-CoV-2 virus spiked into residual, negative nasopharyngeal swab (NPS) matrix. Clinical performance was assessed with 48 positive and 50 negative frozen retrospective samples using the Direct Extraction protocol compared to an external Emergency Use Authorized (EUA) comparator assays (cobas® Liat® SARS-CoV-2 &amp; Influenza A/B assay and the Hologic Panther Fusion® SARS-CoV-2 Assay respectively) on three PCR platforms. The Direct Extraction protocol was evaluated for performance in the presence of 13 potentially interfering substances that can be present in a respiratory specimen. Results The LoD of the Direct Extraction protocol ranges from 1000 – 3000 genomic equivalents (GE)/mL. The clinical performance of the assay was 95.8% positive agreement (95% CI of 84.6% - 99.3%) and 100% negative agreement (95% CI of 90.9% - 100% or 91.1% – 100%) across all three PCR platforms tested. The viral target was detected at 3X LoD for all interferents tested. Conclusion The Direct Extraction protocol of ChromaCode’s SARS-CoV-2 Assay is a sensitive test that eliminates the need for sample extraction and performs very well against traditional extraction-based workflows. The inclusion of this protocol can reduce costs, reliance on extraction systems, and time associated with extraction-based protocols. Disclosures Meghna Yadav, Ph.D. Molecular Biology, ChromaCode Inc. (Employee, Shareholder) Tiffany Martinez, n/a, ChromaCode (Employee, Shareholder) Isabel Regoli, MS, Bioinformatics, ChromaCode (Employee, Shareholder) Osvaldo Hernandez, B.S., Molecular Biology, ChromaCode (Employee, Shareholder) Phuong Le, B.S., Biochemistry, ChromaCode (Employee, Shareholder) Heather Carolan, Masters, Computational Molecular Biology, ChromaCode (Employee, Shareholder) Brad Brown, Ph.D Biomedical Sciences, ChromaCode (Employee, Shareholder) Karen Menge, Ph.D. Biochemistry, ChromaCode (Employee, Shareholder)ChromaCode (Employee, Shareholder)

Et Özgüllüğünün Belirlenmesinde Primer Setlerinin Tasarımına Yönelik Biyoinformatik Tabanlı Bir Yaklaşım

Polymerase chain reaction (PCR) and its derivatives are one of the most widely used DNA-based methods in species determination studies in meat and meat products. Chromosomal or mitochondrial genes of the species can be targeted in PCR-based analyzes used in species detection studies. Many researchers are able to realize oligonucleotide differences between species through online alignment programs on mitochondrial DNA. Using chromosomal DNA would provide more concise results in quantification studies. However, determining the marker regions for genomic DNA is challenging due to the large size of the chromosomes. Bioinformatics approaches are available for selected applications. However, using those approaches requires intensive knowledge of computer science, molecular biology, and bioinformatics in addition to high computational power. In this study, a pipeline is presented that will provide a user-friendly approach to be adopted by facilities where contamination analyzes are routinely performed.

A novel method for inference of acyclic chemical compounds with bounded branch-height based on artificial neural networks and integer programming

Analysis of chemical graphs is becoming a major research topic in computational molecular biology due to its potential applications to drug design. One of the major approaches in such a study is inverse quantitative structure activity/property relationship (inverse QSAR/QSPR) analysis, which is to infer chemical structures from given chemical activities/properties. Recently, a novel two-phase framework has been proposed for inverse QSAR/QSPR, where in the first phase an artificial neural network (ANN) is used to construct a prediction function. In the second phase, a mixed integer linear program (MILP) formulated on the trained ANN and a graph search algorithm are used to infer desired chemical structures. The framework has been applied to the case of chemical compounds with cycle index up to 2 so far. The computational results conducted on instances with n non-hydrogen atoms show that a feature vector can be inferred by solving an MILP for up to n=40, whereas graphs can be enumerated for up to n=15. When applied to the case of chemical acyclic graphs, the maximum computable diameter of a chemical structure was up to 8. In this paper, we introduce a new characterization of graph structure, called “branch-height” based on which a new MILP formulation and a new graph search algorithm are designed for chemical acyclic graphs. The results of computational experiments using such chemical properties as octanol/water partition coefficient, boiling point and heat of combustion suggest that the proposed method can infer chemical acyclic graphs with around n=50 and diameter 30.

JCB Call for Papers for the Special Issue on “40 Years of Computational Biology: In Honor of Professor Michael Waterman's 80th Birthday”

Journal of Computational BiologyVol. 28, No. 7 Call for PapersFree AccessJCB Call for Papers for the Special Issue on “40 Years of Computational Biology: In Honor of Professor Michael Waterman's 80th Birthday”Deadline for Intention to submit: August 1, 2021 Deadline for Manuscript Submission: December 1, 2021Published Online:15 Jul 2021https://doi.org/10.1089/cmb.2021.29043.cfpAboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail Professor Waterman is one of the founders of the field of computational biology and has made many fundamental contributions to the field. The well-known Smith-Waterman algorithm for sequence comparison based on dynamic programing algorithm made large-scale sequence comparison and database searches possible. His studies on the statistical properties of alignment scores between random sequences led to methods to theoretically evaluate the statistical significance of aligned sequences. The much celebrated Lander-Waterman model on physical mapping contributed to many genome projects including the human genome project. The Eulerian graph method for sequence assembly (also called the De Bruijn Method) that he initiated has been the dominant approach for the analysis of next generation sequencing data. He also made significant contributions to the algorithmic approaches to RNA structure prediction, mathematics of phylogeny, statistical distributions of words and motifs in sequences, haplotype block partition, alignment-free genome comparison, and many other mathematical or computational solutions to biological problems.In addition to his own research, he trained many postdoctoral fellows and students who became leaders in the field of computational biology including Gary Churchill at Jackson Laboratory, Pavel Pevzner at UCSD, Sophie Schbath at INRA, Martin Vingron at Max Planck Institute for Molecular Genetics, and Tandy Warnow at University of Illinois to name a few. He led a NIH Center of Excellence in Genomic Science at USC from 2003–2009 and co-led the center until 2015. Professor Waterman trained a large number of minority students during this period. He also established the PhD in Computational Biology and Bioinformatics (CBB) program and contributed to the establishment of the Quantitative Biology (QBIO) Bachelor's of Science and Quantitative and Computational Biology (QCB) Master's of Science degrees at USC. Professor Waterman's institutional legacy is the new USC Department of Quantitative and Computational Biology (https://www.qcb-dornsife.usc.edu).Michael Waterman made extensive efforts to form the computational biology community across the world since the inception of the field. In 1994, he started the Journal of Computational Biology and served as its Editor-in-Chief until 2021. In 1997, he established the annual international conference on “Research in Computational Molecular Biology (RECOMB)” with JCB as its major publication venue.For his multiple outstanding contributions, he received many awards including election as member of US National Academy of Sciences, US National Academy of Engineering, US National Academy of Inventors, French Académie des Sciences, and Chinese Academy of Sciences. He received a Gairdner Foundation International Award, the Dan David Prize in Bioinformatics from Tel Aviv University and the William Benter Prize in Applied Mathematics from City University of Hong Kong.To honor Michael Waterman's contributions to teaching, research and service to the international computational biology community, especially to JCB and the RECOMB conference, JCB is pleased to publish a special issue in honor of his 80th birthday. Friends, colleagues and students who have been associated with him in any way are welcome to submit original and review papers to JCB. Please choose “Waterman Special Issue” as the Manuscript Type when submitting.Any topics related to computational biology are welcome. Some examples include (but are not limited to): Molecular sequence analysisSequencing and genotyping technologiesRegulation and epigenomicsTranscriptomics, including single-cellMetagenomicsPopulation and statistical geneticsEvolutionary, compressive and comparative genomicsStructure and function of RNAsComputational proteomicsProtein structure and functionBiological networksComputational systems biologyPrivacy of biomedical dataBioimagingDeadlines:Intention to submit: August 1, 2021. Please email Fengzhu Sun at fsun@usc.edu to let him know your intention to submit to the special issue.Paper Submission Deadline: December 1, 2021Completion of First Review: February 1, 2021Completion of Final Review: April 1, 2022Target Publication: July 2022Submission contact:Ms. Sheryl NaleEmail: jcb@liebertpub.comGuest Editors:Dr. Sorin IstrailBrown University, USADr. Pavel PevznerUniversity of California San Diego, USADr. Fengzhu SunUniversity of Southern California, USADr. Martin VingronMax Planck Institute for Molecular Genetics, GermanyManuscript Submission Instructions:To begin your submission, please go directly to https://mc.manuscriptcentral.com/jcb. If you do not have an account, you will need to create one. Once you are signed in, click on “Author” and “Start New Submission.” From there, you will be prompted for specific information regarding the paper (title, etc.). Under Manuscript Type, you must select Waterman Special Issue from the drop-down menu. Please visit the Instructions for Authors at https://home.liebertpub.com/publications/journal-of-computational-biology/31/for-authors for complete details to prepare your manuscript.Manuscripts must be prepared in either LaTex format or Microsoft Word.The entire manuscript must be double spaced, Font size 12, including references and tables. Clearly identify unusual or Greek letters.The Main Document generally includes:1.Title Page: Title, Authors, Affiliations [include all email(s)], and Running Head2.Abstract: 250 words or less with 5 Keywords3.Introduction4.Methods5.Results6.Discussion7.Acknowledgements/Disclosure/Conflict of Interest Statements8.References in JCB specified format9.Tables (1 per page)10.Figure Legends (1 list)11.Figures (EPS format, 300 DPI resolution, CMYK Color) (1 file per figure)For LaTex Submissions:2Options for uploading files (2nd one is preferred):1.Upload the main LaTeX file first, selecting the file designation of Main Document.Then, upload each file that is related to the main.TEX file as is needed for processing, selecting the file designation of TeX/LaTeX Suppl File.Be sure to upload all style sheets, and reference and bibliography files that are a part of the document.All figure/image files must be saved and uploaded in .EPS format in order for the file to properly format and convert.Check the system-generated PDF of your manuscript; if all characters do not convert properly, use the 2nd option below.2.Authors may choose to upload a complete, full text (including figures and tables) PDF under the file designation “Full Manuscript PDF”.The LaTex source files should be uploaded as a zip file as Supplementary Data, with the zip file name “SourceFiles.zip”.We look forward to receiving your manuscript and publishing it in our upcoming special issue honoring Professor Michael Waterman.Please feel free to contact the Editorial Office with any questions you may have.□ Mary Ann Liebert, Inc., publishers, 140 Huguenot Street, 3rd Floor, New Rochelle, NY 10801-5215, USA.Visit the Instructions for Authors:www.liebertpub.com/cmbSubmit your paper for peer review online:https://mc.manuscriptcentral.com/jcbFiguresReferencesRelatedDetails Volume 28Issue 7Jul 2021 InformationCopyright 2021, Mary Ann Liebert, Inc., publishersTo cite this article:JCB Call for Papers for the Special Issue on “40 Years of Computational Biology: In Honor of Professor Michael Waterman's 80th Birthday”.Journal of Computational Biology.Jul 2021.633-635.http://doi.org/10.1089/cmb.2021.29043.cfpPublished in Volume: 28 Issue 7: July 15, 2021Online Ahead of Print:June 29, 2021PDF download

Closure properties of bonded sequential insertion-deletion systems

Through the years, formal language theory has evolved through continual interdisciplinary work in theoretical computer science, discrete mathematics and molecular biology. The combination of these areas resulted in the birth of DNA computing. Here, language generating devices that usually considered any set of letters have taken on extra restrictions or modified constructs to simulate the behavior of recombinant DNA. A type of these devices is an insertion-deletion system, where the operations of insertion and deletion of a word have been combined in a single construct. Upon appending integers to both sides of the letters in a word, bonded insertion-deletion systems were introduced to accurately depict chemical bonds in chemical compounds. Previously, it has been shown that bonded sequential insertion-deletion systems could generate up to recursively enumerable languages. However, the closure properties of these systems have yet to be determined. In this paper, it is shown that bonded sequential insertion-deletion systems are closed under union, concatenation, concatenation closure, λ-free concatenation closure, substitution and intersection with regular languages. Hence, the family of languages generated by bonded sequential insertion-deletion systems is shown to be a full abstract family of languages.

Biopython: Comparing the DNA Polymerase I (polA) Gene of Thermophilic, Mesophilic, and Psychrophilic Bacteria

Biopython is a specialized Python tool for computational molecular biology. Various computational molecular analysis that can be performed using Biopython, such as reconstructing phylogenetic trees, multiple sequence analysis, generating complementary sequences, counting amino acids, etc. This technical notes paper describes in detail the procedures computational DNA sequence analysis using Biopython. The DNA polymerase I (polA) gene sequences of bacteria were used in this study to compare the differences between Thermophilic, Mesophilic, and Psychrophilic bacteria.