This investigation was financially supported by the National Natural Science Foundation of China (81925026, 82002201, 81800746, 82102508).

1. The installation of the virtual machine
<ol>
	<li>Download the virtual machine file from (https://github.com/zhenchengfang/DL-VM).</li>
	<li>Download the VirtualBox software from&#160;https://www.virtualbox.org.</li>
	<li>Decompress the &#34;.7z&#34; file using related software, such as &#34;7-Zip&#34;, &#34;WinRAR&#34; or &#34;WinZip&#34;.</li>
	<li>Install the VirtualBox software by clicking the Next button in each step.</li>
	<li>Open the VirtualBox software and click the New button to create a virtual machine.</li>
	<li>Step 6: Enter the specified virtual machine name in the &#34;Name&#34; frame, select Linux as the operating system in the &#34;Type&#34; frame, select Ubuntu in the &#34;Version&#34; frame and click the Next button.</li>
	<li>Allocate the memory size of the virtual machine. We recommend that users pull the button to the right-most part of the green bar to assign as much memory as possible to the virtual machine, and then click the Next button.</li>
	<li>Choose the Use an existing virtual hard disk file selection, select the file &#34;VM_Bioinfo.vdi&#34; downloaded from Step 1.1 and then click the Create button.</li>
	<li>Click the Star button to open the virtual machine. 
	&#8203;NOTE: Figure 3 shows the screenshot of the desktop of the virtual machine.</li>
</ol>
2. Create shared folders for files exchanging between the physical host and the virtual machine
<ol>
	<li>In the physical host, create a shared folder named &#34;shared_host&#34;, and on the desktop of the virtual machine, create a shared folder named &#34;shared_VM&#34;.</li>
	<li>In the Menu Bar of the virtual machine, click Devices, Shared Folder, Shared Folders Settings successively.</li>
	<li>Click the button in the upper right corner.</li>
	<li>Select the shared folder in the physical host created in Step 2.1 and select the Auto-mount option. Click the OK button.</li>
	<li>Restart the virtual machine.</li>
	<li>Click the right click on the desktop of the virtual machine and open the terminal.</li>
	<li>Copy the follow command to the terminal: 
	&#8203;sudo mount -t vboxsf shared_host ./Desktop/shared_VM
	<ol>
		<li>When prompted for a password, enter &#34;1&#34; and hit the &#34;Enter&#34; key, as shown in Figure 4.</li>
	</ol>
	</li>
</ol>
3. Prepare the files for the training set and test set
<ol>
	<li>Copy all four sequence files in &#34;fasta&#34; format for the training and testing process to the &#34;shared_host&#34; folder of the physical host. In this way, all the files will also occur in the &#34;shared_VM&#34; folder of the virtual machine. Then, copy the files in the &#34;shared_VM&#34; folder to the &#34;DeepLearning&#34; folder of the virtual machine.</li>
</ol>
4. Digitize the biological sequences using &#34;one-hot&#34; encoding form
<ol>
	<li>Go to the &#34;DeepLearning&#34; folder, click the right click and open the terminal. Type the following command: 
	./onehot_encoding p_train.fasta n_train.fasta p_test.fasta n_test.fasta aa 
	(for amino acid sequences) 
	or 
	./onehot_encoding p_train.fasta n_train.fasta p_test.fasta n_test.fasta nt 
	(for nucleic acid sequences) 
	&#8203;NOTE: A screenshot of this process is provided in Figure 5.</li>
</ol>
5. Train and test the artificial neural network
<ol>
	<li>In the terminal, type the following command as shown in Figure 6: 
	python train.py 
	NOTE: The training process will begin.</li>
</ol>

<ol>
	<li>Liang, Q., Bible, P. W., Liu, Y., Zou, B., Wei, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DeepMicrobes:+taxonomic+classification+for+metagenomics+with+deep+learning.">DeepMicrobes: taxonomic classification for metagenomics with deep learning.</a> NAR Genomics and Bioinformatics. 2 (1), (2020).</li><li>Ren, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=VirFinder:+a+novel+k+-mer+based+tool+for+identifying+viral+sequences+from+assembled+metagenomic+data.">VirFinder: a novel k -mer based tool for identifying viral sequences from assembled metagenomic data.</a> Microbiome. 5 (1), 69 (2017).</li><li>Fang, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PPR-Meta:+a+tool+for+identifying+phages+and+plasmids+from+metagenomic+fragments+using+deep+learning.">PPR-Meta: a tool for identifying phages and plasmids from metagenomic fragments using deep learning.</a> GigaScience. 8 (6), (2019).</li><li>Ren, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identifying+viruses+from+metagenomic+data+using+deep+learning.">Identifying viruses from metagenomic data using deep learning.</a> Quantitative Biology. 8 (1), 64-77 (2020).</li><li>Zhou, F., Xu, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=cBar:+a+computer+program+to+distinguish+plasmid-derived+from+chromosome-derived+sequence+fragments+in+metagenomics+data.">cBar: a computer program to distinguish plasmid-derived from chromosome-derived sequence fragments in metagenomics data.</a> Bioinformatics. 26 (16), 2051-2052 (2010).</li><li>Krawczyk, P. S., Lipinski, L., Dziembowski, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PlasFlow:+predicting+plasmid+sequences+in+metagenomic+data+using+genome+signatures.">PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures.</a> Nucleic Acids Research. 46 (6), (2018).</li><li>Pellow, D., Mizrahi, I., Shamir, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PlasClass+improves+plasmid+sequence+classification.">PlasClass improves plasmid sequence classification.</a> PLOS Computational Biology. 16 (4), (2020).</li><li>Arango-Argoty, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DeepARG:+a+deep+learning+approach+for+predicting+antibiotic+resistance+genes+from+metagenomic+data.">DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.</a> Microbiome. 6 (1), 1-15 (2018).</li><li>Zheng, D., Pang, G., Liu, B., Chen, L., Yang, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Learning+transferable+deep+convolutional+neural+networks+for+the+classification+of+bacterial+virulence+factors.">Learning transferable deep convolutional neural networks for the classification of bacterial virulence factors.</a> Bioinformatics. 36 (12), 3693-3702 (2020).</li><li>LeCun, Y., Bengio, Y., Hinton, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+learning.">Deep learning.</a> Nature. 521 (7553), 436-444 (2015).</li><li>Fang, Z., Zhou, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=VirionFinder:+Identification+of+Complete+and+Partial+Prokaryote+Virus+Virion+Protein+From+Virome+Data+Using+the+Sequence+and+Biochemical+Properties+of+Amino+Acids.">VirionFinder: Identification of Complete and Partial Prokaryote Virus Virion Protein From Virome Data Using the Sequence and Biochemical Properties of Amino Acids.</a> Frontiers in Microbiology. 12, 615711 (2021).</li><li>Fang, Z., Zhou, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+the+conjugative+and+mobilizable+plasmid+fragments+in+the+plasmidome+using+sequence+signatures.">Identification of the conjugative and mobilizable plasmid fragments in the plasmidome using sequence signatures.</a> Microbial Genomics. 6 (11), (2020).</li><li>Richter, D. C., Ott, F., Auch, A. F., Schmid, R., Huson, D. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MetaSim-a+sequencing+simulator+for+genomics+and+metagenomics.">MetaSim-a sequencing simulator for genomics and metagenomics.</a> PLoS One. 3 (10), 3373 (2008).</li><li>Zhang, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prediction+of+virus-host+infectious+association+by+supervised+learning+methods.">Prediction of virus-host infectious association by supervised learning methods.</a> BMC Bioinformatics. 18 (3), 143-154 (2017).</li></ol>

The authors declare that there are no conflicts of interest.

This tutorial provides an overview for biologists and algorithm design beginners on how to construct an easy-to-use deep learning framework for biological sequence classification in metagenomic data. This tutorial aims to provide intuitive understanding of deep learning and address the challenge that beginners often have difficulty installing the deep learning package and writing the code for the algorithm. For some simple classification tasks, users can use the framework to perform the classification tasks.
<p class...

The metagenomic sequencing technique bypasses the strain isolation process and directly sequences the total DNA in an environmental sample. Thus, metagenomic data contain DNA from different organisms, and most biological sequences are from novel organisms that are not present in the current database. According to different research purposes, biologists need to classify these sequences from different perspectives, such as taxonomic classification1, virus-bacteria classification2,3,4, chromosome-plasmid classification3,5,6,7, and gene function annotation (such as antibiotic resistance gene classification8 and virulence factor classification9). Because metagenomic data contain a large number of novel species and genes, ab initio algorithms, which do not rely on known databases for sequence classification (including DNA classification and protein classification), are an important approach in metagenomic data analysis. However, the design of such algorithms requires professional mathematics knowledge and programming skills; therefore, many biologists and algorithm design beginners have difficulty constructing a classification algorithm to suit their own needs.
With the development of artificial intelligence, deep learning algorithms have been widely used in the field of bioinformatics to complete tasks such as sequence classification in metagenomic analysis. To help beginners understand deep learning algorithms, we describe the algorithm in an easy-to-understand fashion below.
An overview of a deep learning technique is shown in Figure 1. The core technology of a deep learning algorithm is an artificial neural network, which is inspired by the structure of the human brain. From a mathematical point of view, an artificial neural network may be regarded as a complex function. Each object (such as a DNA sequence, a photo or a video) is first digitized. The digitized object is then imported to the function. The task of the artificial neural network is to give a correct response according to the input data. For example, if an artificial neural network is constructed to perform a 2-class classification task, the network should output a probability score that is between 0-1 for each object. The neural network should give the positive object a higher score (such as a score higher than 0.5) while giving the negative object a lower score. To obtain this goal, an artificial neural network is constructed with the training and testing processes. During these processes, data from the known database are downloaded and then divided into a training set and test set. Each object is digitized in a proper way and given a label (&#34;1&#34; for positive objects and &#34;0&#34; for negative objects). In the training process, the digitized data in the training set are inputted into the neural network. The artificial neural network constructs a loss function that represents the dissimilarity between the output score of the input object and the corresponding label of the object. For example, if the label of the input object is &#34;1&#34; while the output score is &#34;0.1&#34;, the loss function will be high; and if the label of the input object is &#34;0&#34; while the output score is &#34;0.1&#34;, the loss function will be low. The artificial neural network employs a specific iterative algorithm that adjusts the parameters of the neural network to minimize the loss function. The training process finishes when the loss function cannot be obviously further decreased. Finally, the data in the test set are used to test the fixed neural network, and the ability of the neural network to calculate the correct labels for the novel objects is evaluated. More principles of deep learning algorithms can be found in the review in LeCun et al.10.
Although the mathematical principles of deep learning algorithms may be complex, many highly packaged deep learning packages have recently been developed, and programmers can directly construct a simple artificial neural network with a few lines of code.
To assist biologists and algorithm design beginners in getting started in using deep learning more quickly, this tutorial provides a guideline for constructing an easy-to-use deep learning framework for sequence classification. This framework uses the &#34;one-hot&#34; encoding form as the mathematical model to digitize the biological sequences and uses a convolution neural network to perform the classification task (see the Supplementary Material). The only thing that the users need to do before using this guideline is to prepare four sequence files in &#34;fasta&#34; format. The first file contains all sequences of the positive class for the training process (referred to &#34;p_train.fasta&#34;); the second file contains all sequences of the negative class for the training process (referred to &#34;n_train.fasta&#34;); the third file contains all sequences of the positive class for the testing process (referred to &#34;p_test.fasta&#34;); and the last file contains all sequences of the negative class for the testing process (referred to &#34;n_test.fasta&#34;). The overview of the flowchart of this tutorial is provided in Figure 2, and more details will be mentioned below.

<table><tbody><tr><td>PC or server</td><td>NA</td><td>NA</td><td>Suggested memory: &gt;6GB</td></tr><tr><td>VirtualBox software</td><td>NA</td><td>NA</td><td>Link: https://www.virtualbox.org</td></tr></tbody></table>

a virtual machine platform for non-computer professionals for using deep learning to classify biological sequences of metagenomic data

A variety of biological sequence classification tasks, such as species classification, gene function classification and viral host classification, are expected processes in many metagenomic data analyses. Since metagenomic data contain a large number of novel species and genes, high-performing classification algorithms are needed in many studies. Biologists often encounter challenges in finding suitable sequence classification and annotation tools for a specific task and are often not able to construct a corresponding algorithm on their own because of a lack of the necessary mathematical and computational knowledge. Deep learning techniques have recently become a popular topic and show strong advantages in many classification tasks. To date, many highly packaged deep learning packages, which make it possible for biologists to construct deep learning frameworks according to their own needs without in-depth knowledge of the algorithm details, have been developed. In this tutorial, we provide a guideline for constructing an easy-to-use deep learning framework for sequence classification without the need for sufficient mathematical knowledge or programming skills. All the code is optimized in a virtual machine so that users can directly run the code using their own data.

In our previous work, we developed a series of sequence classification tools for metagenomic data using an approach similar to this tutorial3,11,12. As an example, we deposited the sequence files of the subset of training set and test set from our previous work3,11 in the virtual machine.
Fang &#38; Zhou11 aimed to iden...

Watch this Scientific Journal Video about A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data at JoVE.com

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

This tutorial describes a simple method to construct a deep learning algorithm for performing 2-class sequence classification of metagenomic data.

A variety of biological sequence classification tasks, such as species classification, gene function classification and viral host classification, are ...

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Research

JoVE Journal

Genetics

3.9K Views.  Southern Medical University. Een verscheidenheid aan biologische sequentieclassificatie taken, zoals soort classificatie, gen functie classificatie, en draad gastheer classificatie zijn verwachte processen in veel metagenomic data analyses. Aangezien metagenomische gegevens een groot aantal Novo-soorten en -genen bevatten, zijn in veel studies goed presterende classificatieorganismen nodig. Biologen ondervinden vaak uitdagingen bij het vinden van geschikte sequentieclassificatie- en notatietools voor een specifieke taak ...

Video: A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Metagenomic Analysis of Silage

Metagenomics is defined as the direct analysis of deoxyribonucleic acid (DNA) purified from environmental samples and enables taxonomic identification of the microbial communities present within them. Two main metagenomic approaches exist; sequencing the 16S rRNA gene coding region, which exhibits sufficient variation between taxa for identification, and shotgun sequencing, in which genomes of the organisms that are present in the sample are analyzed and ascribed to "operational taxonomic units"; species, genera or families depending on the extent of sequencing coverage. 
In this study, shotgun sequencing was used to analyze the microbial community present in cattle silage and, coupled with a range of bioinformatics tools to quality check and filter the DNA sequence reads, perform taxonomic classification of the microbial populations present within the sampled silage, and achieve functional annotation of the sequences. These methods were employed to identify potentially harmful bacteria that existed within the silage, an indication of silage spoilage. If spoiled silage is not remediated, then upon ingestion it could be potentially fatal to the livestock.

Metagenomics was used to investigate the microbiome of silage cattle feed. Analysis was performed by shotgun sequencing. This approach was used to characterize the composition of the microbial community within the cattle feed.

Metagenomics is defined as the direct analysis of deoxyribonucleic acid (DNA) purified from environmental samples and enables taxonomic identification of ...

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics. It also serves as a primer for biologists to develop data scientist computational skills, e.g. executing bash commands, visualization and management of large data sets. All command line code and further explanations of each command or step can be found on the wiki (<a href="https://wiki.cyverse.org/wiki/x/dgGtAQ">https://wiki.cyverse.org/wiki/x/dgGtAQ</a>). The Discovery Environment and Atmosphere platforms are connected together through the CyVerse Data Store. As such, once the initial raw sequencing data has been uploaded there is no more need to transfer large data files over an Internet connection, minimizing the amount of time needed to conduct analyses. This protocol is designed to analyze only two experimental treatments or conditions. Differential gene expression analysis is conducted through pairwise comparisons, and will not be suitable to test multiple factors. This workflow is also designed to be manual rather than automated. Each step must be executed and investigated by the user, yielding a better understanding of data and analytical outputs, and therefore better results for the user. Once complete, this protocol will yield de novo assembled transcriptome(s) for underserved (non-model) organisms without the need to map to previously assembled reference genomes (which are usually not available in underserved organism). These de novo transcriptomes are further used in pairwise differential gene expression analysis to investigate genes differing between two experimental conditions. Differentially expressed genes are then functionally annotated to understand the genetic response organisms have to experimental conditions. In total, the data derived from this protocol is used to test hypotheses about biological responses of underserved organisms.

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

This protocol outlines a comparative de novo transcriptome assembly and annotation workflow for novice bioinformaticians. The workflow is available for free entirely through CyVerse and connected by the Data Store. Command line and graphical user interfaces are used, but all code needed is available to copy and paste.

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative ...

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged as a powerful tool for gene-expression analysis and transcriptome mapping. However, handling RNA-Seq datasets requires sophisticated computational expertise and poses inherent challenges for biology researchers. This bottleneck has been mitigated by the open access Galaxy project that allows users without bioinformatics skills to analyze RNA-Seq data, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID), a Gene Ontology (GO) term analysis suite that helps derive biological meaning from large data sets. However, for first-time users and bioinformatics' amateurs, self-learning and familiarization with these platforms can be time-consuming and daunting. We describe a straightforward workflow that will help C. elegans researchers to isolate worm RNA, conduct an RNA-Seq experiment and analyze the data using Galaxy and DAVID platforms. This protocol provides stepwise instructions for using the various Galaxy modules for accessing raw NGS data, quality-control checks, alignment, and differential gene expression analysis, guiding the user with parameters at every step to generate a gene list that can be screened for enrichment of gene classes or biological processes using DAVID. Overall, we anticipate that this article will provide information to C. elegans researchers undertaking RNA-Seq experiments for the first time as well as frequent users running a small number of samples.

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Galaxy and DAVID have emerged as popular tools that allow investigators without bioinformatics training to analyze and interpret RNA-Seq data. We describe a protocol for C. elegans researchers to perform RNA-Seq experiments, access and process the dataset using Galaxy and obtain meaningful biological information from the gene lists using DAVID.

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged ...

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing high-throughput short-read DNA sequencing. The resulting sequence datasets can not only track what genes in a population that are enriched during selection for positive yeast 2-hybrid interactions, but also give detailed information about the relevant subdomains of proteins sufficient for interaction. Here, we describe a full suite of stand-alone software programs that allow non-experts to perform all the bioinformatics and statistical steps to process and analyze DNA sequence fastq files from a batch yeast 2-hybrid assay. The processing steps covered by these software include: 1) mapping and counting sequence reads corresponding to each candidate protein encoded within a yeast 2-hybrid prey library; 2) a statistical analysis program that evaluates the enrichment profiles; and 3) tools to examine the translational frame and position within the coding region of each enriched plasmid that encodes the interacting proteins of interest.

Deep sequencing of yeast populations selected for positive yeast 2-hybrid interactions potentially yields a wealth of information about interacting partner proteins. Here, we describe the operation of specific bioinformatics tools and customized updated software to analyze sequence data from such screens.

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing ...

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried out by Public Health laboratories and regulatory agencies. Large volumes of publicly available WGS data can be used to study pathogenic populations at a large scale. Recently, a freely available computational platform called ProkEvo was published to enable reproducible, automated, and scalable hierarchical-based population genomic analyses using bacterial WGS data. This implementation of ProkEvo demonstrated the importance of combining standard genotypic mapping of populations with mining of accessory genomic content for ecological inference. In particular, the work highlighted here used ProkEvo-derived outputs for population-scaled hierarchical analyses using the R programming language. The main objective was to provide a practical guide for microbiologists, ecologists, and epidemiologists by showing how to: i) use a phylogeny-guided mapping of hierarchical genotypes; ii) assess frequency distributions of genotypes as a proxy for ecological fitness; iii) determine kinship relationships and genetic diversity using specific genotypic classifications; and iv) map lineage differentiating accessory loci. To enhance reproducibility and portability, R markdown files were used to demonstrate the entire analytical approach. The example dataset contained genomic data from 2,365 isolates of the zoonotic foodborne pathogen Salmonella Newport. Phylogeny-anchored mapping of hierarchical genotypes (Serovar -&#62; BAPS1 -&#62; ST -&#62; cgMLST) revealed the population genetic structure, highlighting sequence types (STs) as the keystone differentiating genotype. Across the three most dominant lineages, ST5 and ST118 shared a common ancestor more recently than with the highly clonal ST45 phylotype. ST-based differences were further highlighted by the distribution of accessory antimicrobial resistance (AMR) loci. Lastly, a phylogeny-anchored visualization was used to combine hierarchical genotypes and AMR content to reveal the kinship structure and lineage-specific genomic signatures. Combined, this analytical approach provides some guidelines for conducting heuristic bacterial population genomic analyses using pan-genomic information.

This analytical computational platform provides practical guidance for microbiologists, ecologists, and epidemiologists interested in bacterial population genomics. Specifically, the work presented here demonstrated how to perform: i) phylogeny-guided mapping of hierarchical genotypes; ii) frequency-based analysis of genotypes; iii) kinship and clonality analyses; iv) identification of lineage differentiating accessory loci.

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried ...

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Pathogens can cause a wide variety of infectious diseases. The biological processes induced by the host in response to infection determine the severity of the disease.&#160;To study such processes, researchers can use high-throughput sequencing techniques (RNA-seq) that measure the dynamic changes of the host transcriptome at different stages of infection, clinical outcomes, or disease severity.This investigation can lead to a better understanding of the diseases, as well as uncovering potential drug targets and treatments. The protocol presented here describes a complete pipeline to analyze RNA-sequencing data from raw reads to functional analysis. The pipeline is divided into five steps: (1) quality control of the data; (2) mapping and annotation of genes; (3) statistical analysis to identify differentially expressed genes and co-expressed genes; (4) determination of the molecular degree of the perturbation of samples; and (5) functional analysis. Step 1 removes technical artifacts that may impact the quality of downstream analyses. In step 2, genes are mapped and annotated according to standard library protocols.&#160;The statistical analysis in step 3 identifies genes that are differentially expressed or co-expressed in infected samples, in comparison with non-infected ones. Sample variability and the presence of potential biological outliers are verified using the molecular degree of perturbation approach in step 4. Finally, the functional analysis in step 5 reveals the pathways associated with the disease phenotype. The presented pipeline aims to support researchers through the RNA-seq data analysis from host-pathogen interaction studies and drive future&#160;in vitro or in vivo experiments, that are essential to understand the molecular mechanism of infections.

The protocol presented here describes a complete pipeline to analyze RNA-sequencing transcriptome data from raw reads to functional analysis, including quality control and preprocessing steps to advanced statistical analytical approaches.

Pathogens can cause a wide variety of infectious diseases. The biological processes induced by the host in response to infection determine the severity of ...

Immunology and Infection

AI Program for Trypanosomiasis Detection and Classification

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Trypanosomiasis is a significant public health problem in several regions across the world, including South Asia and Southeast Asia. The identification of hotspot areas under active surveillance is a fundamental procedure for controlling disease transmission. Microscopic examination is a commonly used diagnostic method. It is, nevertheless, primarily reliant on skilled and experienced personnel. To address this issue, an artificial intelligence (AI) program was introduced that makes use of a hybrid deep learning technique of object identification and object classification neural network backbones on the in-house low-code AI platform (CiRA CORE). The program can identify and classify the protozoan trypanosome species, namely Trypanosoma cruzi, T. brucei, and T. evansi, from oil-immersion microscopic images. The AI program utilizes pattern recognition to observe and analyze multiple protozoa within a single blood sample and highlights the nucleus and kinetoplast of each parasite as specific characteristic features using an attention map.
To assess the AI program&#39;s performance, two unique modules are created that provide a variety of statistical measures such as accuracy, recall, specificity, precision, F1 score, misclassification rate, receiver operating characteristics (ROC) curves, and precision versus recall (PR) curves. The assessment findings show that the AI algorithm is effective at identifying and categorizing parasites. By delivering a speedy, automated, and accurate screening tool, this technology has the potential to transform disease surveillance and control. It could also assist local officials in making more informed decisions on disease transmission-blocking strategies.

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images 

Worldwide medical blood parasites were automatically screened using simple steps on a low-code AI platform. The prospective diagnosis of blood films was improved by using an object detection and classification method in a hybrid deep learning model. The collaboration of active monitoring and well-trained models helps to identify hotspots of trypanosome transmission.

Trypanosomiasis is a significant public health problem in several regions across the world, including South Asia and Southeast Asia. The identification of ...

Bioengineering

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

Photoperiodic diapause is an important adaptation that allows individuals to escape harsh seasonal environments via a series of physiological changes, most notably developmental arrest and reduced metabolism. Global gene expression profiling via RNA-Seq can provide important insights into the transcriptional mechanisms of photoperiodic diapause. The Asian tiger mosquito, Aedes albopictus, is an outstanding organism for studying the transcriptional bases of diapause due to its ease of rearing, easily induced diapause, and the genomic resources available. This manuscript presents a general experimental workflow for identifying diapause-induced transcriptional differences in A. albopictus. Rearing techniques, conditions necessary to induce diapause and non-diapause development, methods to estimate percent diapause in a population, and RNA extraction and integrity assessment for mosquitoes are documented. A workflow to process RNA-Seq data from Illumina sequencers culminates in a list of differentially expressed genes. The representative results demonstrate that this protocol can be used to effectively identify genes differentially regulated at the transcriptional level in A. albopictus due to photoperiodic differences. With modest adjustments, this workflow can be readily adapted to study the transcriptional bases of diapause or other important life history traits in other mosquitoes.

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

RNA-Seq analyses are becoming increasingly important for identifying the molecular underpinnings of adaptive traits in non-model organisms. Here, a protocol to identify differentially expressed genes between diapause and non-diapause Aedes albopictus mosquitoes is described, from mosquito rearing, to RNA sequencing and bioinformatics analyses of RNA-Seq data.

Photoperiodic diapause is an important adaptation that allows individuals to escape harsh seasonal environments via a series of physiological changes, ...

Biology

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

The accessibility of high-throughput sequencing has revolutionized many fields of biology. In order to better understand host-associated viral and microbial communities, a comprehensive workflow for DNA and RNA extraction was developed. The workflow concurrently generates viral and microbial metagenomes, as well as metatranscriptomes, from a single sample for next-generation sequencing. The coupling of these approaches provides an overview of both the taxonomical characteristics and the community encoded functions. The presented methods use Cystic Fibrosis (CF) sputum, a problematic sample type, because it is exceptionally viscous and contains high amount of mucins, free neutrophil DNA, and other unknown contaminants. The protocols described here target these problems and successfully recover viral and microbial DNA with minimal human DNA contamination. To complement the metagenomics studies, a metatranscriptomics protocol was optimized to recover both microbial and host mRNA that contains relatively few ribosomal RNA (rRNA) sequences. An overview of the data characteristics is presented to serve as a reference for assessing the success of the methods. Additional CF sputum samples were also collected to (i) evaluate the consistency of the microbiome profiles across seven consecutive days within a single patient, and (ii) compare the consistency of metagenomic approach to a 16S ribosomal RNA gene-based sequencing. The results showed that daily fluctuation of microbial profiles without antibiotic perturbation was minimal and the taxonomy profiles of the common CF-associated bacteria were highly similar between the 16S rDNA libraries and metagenomes generated from the hypotonic lysis (HL)-derived DNA. However, the differences between 16S rDNA taxonomical profiles generated from total DNA and HL-derived DNA suggest that hypotonic lysis and the washing steps benefit in not only removing the human-derived DNA, but also microbial-derived extracellular DNA that may misrepresent the actual microbial profiles.

Using the cystic fibrosis airway as an example, the manuscript presents a comprehensive workflow comprising a combination of metagenomic and metatranscriptomic approaches to characterize the microbial and viral communities in animal-associated samples.

The accessibility of high-throughput sequencing has revolutionized many fields of biology. In order to better understand host-associated viral and ...

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the immune system. Perturbation of the healthy gut microbiome has been suggested to play a role in the development of inflammatory diseases, including multiple sclerosis (MS). Environmental and genetic factors can influence the composition of the microbiome; therefore, identification of microbial communities linked with a disease phenotype has become the first step towards defining the microbiome&#8217;s role in health and disease. Use of 16S rRNA metagenomic sequencing for profiling bacterial community has helped in advancing microbiome research. Despite its wide use, there is no uniform protocol for 16S rRNA-based taxonomic profiling analysis. Another limitation is the low resolution of taxonomic assignment due to technical difficulties such as smaller sequencing reads, as well as use of only forward (R1) reads in the final analysis due to low quality of reverse (R2) reads. There is need for a simplified method with high resolution to characterize bacterial diversity in a given biospecimen. Advancements in sequencing technology with the ability to sequence longer reads at high resolution have helped to overcome some of these challenges. Present sequencing technology combined with a publicly available metagenomic analysis pipeline such as R-based Divisive Amplicon Denoising Algorithm-2 (DADA2) has helped advance microbial profiling at high resolution, as DADA2 can assign sequence at the genus and species levels. Described here is a guide for performing bacterial profiling using two-step amplification of the V3-V4 region of the 16S rRNA gene, followed by analysis using freely available analysis tools (i.e., DADA2, Phyloseq, and METAGENassist). It is believed that this simple and complete workflow will serve as an excellent tool for researchers interested in performing microbiome profiling studies.

Described here is a simplified standard operating procedure for microbiome profiling using 16S rRNA metagenomic sequencing and analysis using freely available tools. This protocol will help researchers who are new to the microbiome field as well as those requiring updates on methods to achieve bacterial profiling at a higher resolution.

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the ...

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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