Name: a virtual machine platform for non-computer professionals for using deep learning to classify biological sequences of metagenomic data
Uploaded: 2021-09-25T14:00:00
Description: 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

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 but...

<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>

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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>PC or server</td>
			<td>NA</td>
			<td>NA</td>
			<td>Suggested memory: &#62;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 ...

A variety of biological sequence classification task, such as species classification, gene function classification, and wire host classification are expected processes in many metagenomic data analyzes. Since metagenomic data contain a large number of Novo species and genes, high performing classification organisms are needed in many studies. Biologists often encounter challenge in finding suitable sequence classification and notation tools for a specific task and are often not able to construct a corresponding organism 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 advantage in many classification tasks. To date, many highly packaged deep learning package, which make it possible for biologists to construct deep learning frameworks, according to their own needs without in depth knowledge of the organism 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.The following video shows how to use the virtual machine to perform biological sequence classification. Users need to download the virtual machine file from the tutorial homepage, and then download the VirtualBox software. The virtual machine is compressed as a seventy file.The seventy file can easily be decompressed using a current compressing software, such as WinRar, Winzip, and 7-Zip. We decompressed the virtual machine using 7-Zip. The decompression may take some time.Please wait for awhile. After decompression users need to install the VirtualBox software. Create a folder to install the VirtualBox.Create a VirtualBox installation package. Select the folder created by yourself. Then install the VirutalBox software by clicking the next button in each step.The installation may take some time, please wait for awhile. Open the VirtualBox software. Create a new button to create a virtual machine.Enter the virtual machine name specified by yourself in the name frame. Select Linux as the operating system in the type frame. Select Ubuntu in the version frame and click the next button.If possible, allocate a larger amount of memory to the virtual machine. True the use an existing hard disk file selection. Select the virtual machine file downloaded from the tutorial homepage.And then click the create button. Click a start button to open the virtual machine. Starting up the virtual machine may take a while.Please wait for a moment before the next step. Then users need to create shared folder in both physical hosts and virtual machine to exchange files. In your physical host, create a shared folder named shared host and on the desktop of the virtual machine, create a shared folder named shared VM.In the manual bar of the virtual machine, click devices, shared folders, shared folder settings successively.Click the button in the upper right corner. Select the shared folder in the physical host created by yourself. Select the auto mount option.Click the OK button. Then restart the virtual machine. Restarting the virtual machine may take awhile.Please wait for a moment before the next step. Click the right click on the desktop of the virtual machine and open the terminal. Type the following command to the terminal.Sudo, space key, mount, space key, bar T, space key, vboxsf, space key, shared host, space key, dot slash, desktop, slash, shared VM.When prompted for a password, enter one and tap the enter key. Copy all four sequence files in faster format for the training and testing process to the shared host folder of the physical host. In this way, all the files will also occur in the shared VM folder of the virtual machine.Then copy the files in the shared VM folder to the deep learning folder of the virtual machine. Click the right click and open the terminal and type the following command to perform the one hot encoding. Dot slash, one hot encoding, specify the files for training and testing.And specify the sequence type. Then type the following command to start the trending process. Python space key, train dot P Y.Then the trending process will begin.This process may take a few hours or a few days, depending on your data set size. When the process is finished, the predict result of the test data is present in the predict dot CSV file. In our previous work, we developed a series of sequence classification tools for a metagenomic data, using an approach similar to this tutorial.For example, we developed a tool aimed to identify the complete and partial prokaryote virus virion proteins from run data. And a tool aimed to identify phage DNA fragments from bacterial chromosome DNA fragments in metogenomic data. The performance of the tools using the script of this tutorial is shown in the figure a and b.In conclusion, this tutorial provides an overview for biologist and organisms design beginners on how to construct an easy to use deep learning framework for biological sequence classification in metogenomic data. This tutorial aims to provide intuitive understanding of deep learning and address the challenge that beginners often have difficulty in starting the deep learning package and writing the code for the organism. For some simple classification tasks, users can use our framework to perform the classification task.

a-virtual-machine-platform-for-non-computer-professionals-for-using

Research

JoVE Journal

Genetics

A Robotic Platform for High-throughput Protoplast Isolation and Transformation

Over the last decade there has been a resurgence in the use of plant protoplasts that range from model species to crop species, for analysis of signal transduction pathways, transcriptional regulatory networks, gene expression, genome-editing, and gene-silencing. Furthermore, significant progress has been made in the regeneration of plants from protoplasts, which has generated even more interest in the use of these systems for plant genomics. In this work, a protocol has been developed for automation of protoplast isolation and transformation from a &#39;Bright Yellow&#39; 2 (BY-2) tobacco suspension culture using a robotic platform. The transformation procedures were validated using an orange fluorescent protein (OFP) reporter gene (pporRFP) under the control of the Cauliflower mosaic virus 35S promoter (35S). OFP expression in protoplasts was confirmed by epifluorescence microscopy. Analyses also included protoplast production efficiency methods using propidium iodide. Finally, low-cost food-grade enzymes were used for the protoplast isolation procedure, circumventing the need for lab-grade enzymes that are cost-prohibitive in high-throughput automated protoplast isolation and analysis. Based on the protocol developed in this work, the complete procedure from protoplast isolation to transformation can be conducted in under 4 hr, without any input from the operator. While the protocol developed in this work was validated with the BY-2 cell culture, the procedures and methods should be translatable to any plant suspension culture/protoplast system, which should enable acceleration of crop genomics research.

A high-throughput, automated, tobacco protoplast production and transformation methodology is described. The robotic system enables massively parallel gene expression and discovery in the model BY-2 system that should be translatable to non-model crops.

Over the last decade there has been a resurgence in the use of plant protoplasts that range from model species to crop species, for analysis of signal ...

Perturbations of Circulating miRNAs in Irritable Bowel Syndrome Detected Using a Multiplexed High-throughput Gene Expression Platform

The gene expression platform assay allows for robust and highly reproducible quantification of the expression of up to 800 transcripts (mRNA or miRNAs) in a single reaction. The miRNA assay counts transcripts by directly imaging and digitally counting miRNA molecules that are labeled with color-coded fluorescent barcoded probe sets (a reporter probe and a capture probe). Barcodes are hybridized directly to mature miRNAs that have been elongated by ligating a unique oligonucleotide tag (miRtag) to the 3&#39; end. Reverse transcription and amplification of the transcripts are not required. Reporter probes contain a sequence of six color positions populated using a combination of four fluorescent colors. The four colors over six positions are used to construct a gene-specific color barcode sequence. Post-hybridization processing is automated on a robotic prep station. After hybridization, the excess probes are washed away, and the tripartite structures (capture probe-miRNA-reporter probe) are fixed to a streptavidin-coated slide via the biotin-labeled capture probe. Imaging and barcode counting is done using a digital analyzer. The immobilized barcoded miRNAs are visualized and imaged using a microscope and camera, and the unique barcodes are decoded and counted. Data quality control (QC), normalization, and analysis are facilitated by a custom-designed data processing and analysis software that accompanies the assay software. The assay demonstrates high linearity over a broad range of expression, as well as high sensitivity. Sample and assay preparation does not involve enzymatic reactions, reverse transcription, or amplification; has few steps; and is largely automated, reducing investigator effects and resulting in high consistency and technical reproducibility. Here, we describe the application of this technology to identifying circulating miRNA perturbations in irritable bowel syndrome.

We describe the use of a multiplexed high-throughput gene expression platform that quantitates gene expression by barcoding and counting molecules in biological substrates without the need for amplification. We used the platform to quantitate microRNA (miRNA) expression in whole blood in subjects with and without irritable bowel syndrome.

The gene expression platform assay allows for robust and highly reproducible quantification of the expression of up to 800 transcripts (mRNA or miRNAs) in ...

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 ...

Using a GFP-tagged TMEM184A Construct for Confirmation of Heparin Receptor Identity

When novel proteins are identified through affinity-based isolation and bioinformatics analysis, they are often largely uncharacterized. Antibodies against specific peptides within the predicted sequence allow some localization experiments. However, other possible interactions with the antibodies often cannot be excluded. This situation provided an opportunity to develop a set of assays dependent on the protein sequence. Specifically, a construct containing the gene sequence coupled to the GFP coding sequence at the C-terminal end of the protein was obtained and employed for these purposes. Experiments to characterize localization, ligand affinity, and gain of function were originally designed and carried out to confirm the identification of TMEM184A as a heparin receptor1. In addition, the construct can be employed for studies addressing membrane topology questions and detailed protein-ligand interactions. The present report presents a range of experimental protocols based on the GFP-TMEM184A construct expressed in vascular cells that could easily be adapted for other novel proteins.

A construct encoding TMEM184A with a GFP tag at the carboxy-terminus designed for eukaryotic expression, was employed in assays designed to confirm the identification of TMEM184A as a heparin receptor in vascular cells.

When novel proteins are identified through affinity-based isolation and bioinformatics analysis, they are often largely uncharacterized. Antibodies ...

Protocols for C-Brick DNA Standard Assembly Using Cpf1

CRISPR-associated protein Cpf1 cleaves double-stranded DNA under the guidance of CRISPR RNA (crRNA), generating sticky ends. Because of this characteristic, Cpf1 has been used for the establishment of a DNA assembly standard called C-Brick, which has the advantage of long recognition sites and short scars. On a standard C-Brick vector, there are four Cpf1 recognition sites &#8211; the prefix (T1 and T2 sites) and the suffix (T3 and T4 sites) &#8211; flanking biological DNA parts. The cleavage of T2 and T3 sites produces complementary sticky ends, which allow for the assembly of DNA parts with T2 and T3 sites. Meanwhile, a short &#34;GGATCC&#34; scar is generated between parts after assembly. As the newly formed plasmid once again contains the four Cpf1 cleavage sites, the method allows for the iterative assembly of DNA parts, which is similar to those of BioBrick and BglBrick standards. A procedure outlining the use of the C-Brick standard to assemble DNA parts is described here. The C-Brick standard can be widely used by scientists, graduate and undergraduate students, and even amateurs.

CRISPR-associated protein Cpf1 can be guided by a specially designed CRISPR RNA (crRNA) to cleave double-stranded DNA at desired sites, generating sticky ends. Based on this characteristic, a DNA assembly standard (C-Brick) was established, and a protocol detailing its use is described here.

CRISPR-associated protein Cpf1 cleaves double-stranded DNA under the guidance of CRISPR RNA (crRNA), generating sticky ends. Because of this ...

Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with a number of challenges including sample preservation quality, degraded DNA, and destructive sampling of rare specimens. In order to more effectively use herbarium material in large sequencing projects, a dependable and scalable method of DNA isolation and library preparation is needed. This paper demonstrates a robust, beginning-to-end protocol for DNA isolation and high-throughput library construction from herbarium specimens that does not require modification for individual samples. This protocol is tailored for low quality dried plant material and takes advantage of existing methods by optimizing tissue grinding, modifying library size selection, and introducing an optional reamplification step for low yield libraries. Reamplification of low yield DNA libraries can rescue samples derived from irreplaceable and potentially valuable herbarium specimens, negating the need for additional destructive sampling and without introducing discernible sequencing bias for common phylogenetic applications. The protocol has been tested on hundreds of grass species, but is expected to be adaptable for use in other plant lineages after verification. This protocol can be limited by extremely degraded DNA, where fragments do not exist in the desired size range, and by secondary metabolites present in some plant material that inhibit clean DNA isolation. Overall, this protocol introduces a fast and comprehensive method that allows for DNA isolation and library preparation of 24 samples in less than 13 h, with only 8 h of active hands-on time with minimal modifications.

This article demonstrates a detailed protocol for DNA isolation and high-throughput sequencing library construction from herbarium material including rescue of exceptionally poor-quality DNA.

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with ...

CRISPR/Cas9-mediated Targeted Integration In Vivo Using a Homology-mediated End Joining-based Strategy

As a promising genome editing platform, the CRISPR/Cas9 system has great potential for efficient genetic manipulation, especially for targeted integration of transgenes. However, due to the low efficiency of homologous recombination (HR) and various indel mutations of non-homologous end joining (NHEJ)-based strategies in non-dividing cells, in vivo genome editing remains a great challenge. Here, we describe a homology-mediated end joining (HMEJ)-based CRISPR/Cas9 system for efficient in vivo precise targeted integration. In this system, the targeted genome and the donor vector containing homology arms (~800 bp) flanked by single guide RNA (sgRNA) target sequences are cleaved by CRISPR/Cas9. This HMEJ-based strategy achieves efficient transgene integration in mouse zygotes, as well as in hepatocytes in vivo. Moreover, a HMEJ-based strategy offers an efficient approach for correction of fumarylacetoacetate hydrolase (Fah) mutation in the hepatocytes and rescues Fah-deficiency induced liver failure mice. Taken together, focusing on targeted integration, this HMEJ-based strategy provides a promising tool for a variety of applications, including generation of genetically modified animal models and targeted gene therapies.

CRISPR/Cas9-mediated Targeted Integration In Vivo Using a Homology-mediated End Joining-based Strategy

The clustered regularly interspaced short palindromic repeats/CRISPR associated protein 9 (CRISPR/Cas9) system provides a promising tool for genetic engineering, and opens up the possibility of targeted integration of transgenes. We describe a homology-mediated end joining (HMEJ)-based strategy for efficient DNA targeted integration in vivo and targeted gene therapies using CRISPR/Cas9.

As a promising genome editing platform, the CRISPR/Cas9 system has great potential for efficient genetic manipulation, especially for targeted integration ...

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and effect sizes of regulatory elements to a target gene. Some progress has been made with the bioinformatic prediction of the existence and function of proximal epigenetic modifications associated with activated gene expression using conserved transcription factor binding sites. Chromatin conformation capture studies have revolutionized our ability to discover physical chromatin contacts between sequences and even within an entire genome. Circular chromatin conformation capture coupled with next-generation sequencing (4C-seq), in particular, is designed to discover all possible physical chromatin interactions for a given sequence of interest (viewpoint), such as a target gene or a regulatory enhancer. Current 4C-seq strategies directly sequence from within the viewpoint but require numerous and diverse viewpoints to be simultaneously sequenced to avoid the technical challenges of uniform base calling (imaging) with next generation sequencing platforms. This volume of experiments may not be practical for many laboratories. Here, we report a modified approach to the 4C-seq protocol that incorporates both an additional restriction enzyme digest and qPCR-based amplification steps that are designed to facilitate a greater capture of diverse sequence reads and mitigate the potential for PCR bias, respectively. Our modified 4C method is amenable to the standard molecular biology lab for assessing chromatin architecture.

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

The identification of physical interactions between genes and regulatory elements is challenging but has been facilitated by chromosome conformation capture methods. This modification to the 4C-seq protocol mitigates PCR bias by minimizing over-amplification of PCR templates and maximizes the mappability of reads by incorporating an addition restriction enzyme digest step.

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and ...

A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe

A genetic screen for mutant alleles that suppress phenotypic defects caused by a mutation is a powerful approach to identify genes that belong to closely related biochemical pathways. Previous methods such as the Synthetic Genetic Array (SGA) analysis, and random mutagenesis techniques using ultraviolet (UV) or chemicals like ethyl methanesulfonate (EMS) or N-ethyl-N- nitrosourea (ENU), have been widely used but are often costly and laborious. Also, these mutagen-based screening methods are frequently associated with severe side effects on the organism, inducing multiple mutations that add to the complexity of isolating the suppressors. Here, we present a simple and effective protocol to identify suppressor mutations in mutants which confer a growth defect in Schizosaccharomyces pombe. The fitness of cells with a growth deficiency in standard rich liquid media or synthetic liquid media can be monitored for recovery using an automated 96-well plate reader over an extended period. Once a cell acquires a suppressor mutation in the culture, its descendants outcompete those of the parental cells. The recovered cells that have a competitive growth advantage over the parental cells can then be isolated and backcrossed with the parental cells. The suppressor mutations are then identified using whole-genome sequencing. Using this approach, we have successfully isolated multiple suppressors that alleviate the severe growth defects caused by loss of Elf1, an AAA+ family ATPase that is important in nuclear mRNA transport and maintenance of genomic stability. There are currently over 400 genes in S. pombe with mutants conferring a growth defect. As many of these genes are uncharacterized, we propose that our method will hasten the identification of novel functional interactions with this user-friendly, high-throughput approach.

A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe

We present a simple suppressor screen protocol in fission yeast. This method is efficient, mutagen-free, and&#160; selective&#160; for&#160; mutations that&#160; often&#160; occur at&#160; &#160;a&#160; single&#160; genomic&#160; locus.&#160; The protocol is suitable for isolating suppressors that alleviate growth defects in liquid culture that are caused by a mutation or a drug.

A genetic screen for mutant alleles that suppress phenotypic defects caused by a mutation is a powerful approach to identify genes that belong to closely ...

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences

Identifying the sets of transcription factors (TFs) that regulate each human gene is a daunting task that requires integrating numerous experimental and computational approaches. One such method is the yeast one-hybrid (Y1H) assay, in which interactions between TFs and DNA regions are tested in the milieu of the yeast nucleus using reporter genes. Y1H assays involve two components: a &#8216;DNA-bait&#8217; (e.g., promoters, enhancers, silencers, etc.) and a &#8216;TF-prey,&#8217; which can be screened for reporter gene activation. Most published protocols for performing Y1H screens are based on transforming TF-prey libraries or arrays into DNA-bait yeast strains. Here, we describe a pipeline, called enhanced Y1H (eY1H) assays, where TF-DNA interactions are interrogated by mating DNA-bait strains with an arrayed collection of TF-prey strains using a high density array (HDA) robotic platform that allows screening in a 1,536 colony format. This allows for a dramatic increase in throughput (60 DNA-bait sequences against &#62;1,000 TFs takes two weeks per researcher) and reproducibility. We illustrate the different types of expected results by testing human promoter sequences against an array of 1,086 human TFs, as well as examples of issues that can arise during screens and how to troubleshoot them.

Here, we present an enhanced yeast one-hybrid screening protocol to identify the transcription factors (TFs) that can bind to a human DNA region of interest. This method uses a high-throughput screening pipeline that can interrogate the binding of &#62;1,000 TFs in a single experiment.

Identifying the sets of transcription factors (TFs) that regulate each human gene is a daunting task that requires integrating numerous experimental and ...