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\r\nIn part 1, we described the steps involved in obtaining raw sequencing reads from ChIP-Seq libraries. We also explained how to choose an analysis pipeline that best suits your needs. In this post, we describe how to align the raw reads to a reference genome. Let's go!
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Choose an aligner
\r\nThe most important step in alignment is choosing the right 'aligner' tool for your data. We found that reading the user manuals and the theories behind each aligner did not help us much with choosing the right aligner. Each dataset is different, causing an aligner to behave differently. To be confident that we will be using an aligner that best suits our needs, we ran smaller experiments on individual datasets to enable fast inspection of the output. This Wikipedia page lists most of the aligners that we tried in our small-scale experiments. After comparing all the outputs, we decided to choose bowtie 2 (we do not have any affiliation to bowtie or its creators; use at your own risk). The reasons were:
\r\n\r\n\r\nWe recommend you compare different aligners and, perhaps, run a trial with several. Pick the one that gives you the most robust output. For example, low variation among biological/technical replicates and good signal-to-noise ratios.
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Regardless of the aligner that you choose, you will find that two important steps are required before you can start aligning the raw reads.
\r\nBelow we describe these two steps in more detail. Because we used bowtie 2 to align our reads, we will use this aligner as an example. However, the basics should be the same across different aligners.
\r\nFirst, follow the 'getting started' instructions for your chosen aligner. The instructions for bowtie are simple and listed on their website. Briefly, you download the latest version for your operating system from here. To save time, we recommend you download the binary files (if available for the aligner) that can be used as soon as the download is finished. Save the downloaded files in a location that is easily accessible from the 'terminal' (e.g., /Applications/bowtie2).
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The reference genome
\r\nNext, find and download the genome sequence in FASTA format. To get the genome assemblies for bacteria, yeast, worms, and flies, you can visit http://ensemblgenomes.org
\r\nUsually, there is a 'download data' link on the page for the appropriate organism. Clicking on this link will open an FTP connection to the server hosting these files (the hosts permit guest access). Download the appropriate files that cover the entire genome, or parts of the genome that you are interested in and place these files in the same folder as the aligner tool.
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After placing the genome sequence files inside the bowtie folder, we ran the following command to create a reference genome index that can be used by the aligner. The generated index is unique to the aligner, so if you switch to a different aligner tool at a later time, you will need to create the reference genome index again using the method provided by the new aligner. To generate the index with bowtie, we executed the following command in terminal:
\r\nbowtie2-build reference_genome_sequence.fa name_of_indexreference_genome_sequence.fa was placed inside the bowtie folder, and the above command was executed from inside the same folder. If the files are in different locations, don't forget to add the relative paths. Replace 'reference_genome_sequence' with the actual name of the FASTA file that was downloaded. Replace 'name_of_index' with a name of your choice (e.g. my_human_reference_v1).
\r\n\r\n\r\nYou can use the bowtie2-build function to create an index for a set of FASTA files obtained from sites such as UCSC, NCBI, and Ensembl.
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Before creating the index, you can customize the reference genome in any way you want. For example, you may need to add or remove a custom contig at a particular region. Caution: Do not use a text editor for editing the reference genome files (see part 1).
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Align
\r\nAfter the index was created, we used the following 'for-loop' to sequentially perform alignment of each of our sequencing files.
\r\nfor sample in *.fastq.trimmed.gz; \r\ndo \r\necho $sample; \r\ndescriber=$(echo ${sample} | sed 's/.fastq.trimmed.gz//'); \r\necho $describer.sam; \r\n/Applications/bowtie2/bowtie2 -a -x pombe ./${describer}.fastq.trimmed.gz -S ./alighned/${describer}.sam; \r\ndoneThe first line initiates the for-loop for all the files that end with 'fasta.trimmed.gz.' We arbitrarily call each file sample. The * is used to create a wild-card name match. Here, it means process all the file with a name that starts with any string, as long as it ends with fasta.trimmed.gz. Using this approach, we can safely have a bunch of other files (e.g., readme.txt) in the same folder along with the sequence files. However, the for-loop will only process the files that match the given wildcard. 'echo' just prints out the file name that is being processed (stored in the $sample variable). The command on line 4, extracts the first part of the file name (the unique part of the file name that is before fasta.trimmed.gz). We use this to create a new file name with the .sam extension (see below).
\r\nIf you want to align only one file, you can use line 5 (the bowtie2 alignment function) from the above for-loop. In our case, the bowtie2 program was placed inside the Applications folder, and we used the full path to call its function (/Applications/bowtie2/bowtie2). The -a flag is optional and is described below. The -x flag is required and indicates the name of the index file that we created earlier (pombe). The path to the raw sequence files is placed after the index name (./example.fastq.trimmed.gz). In our case, it was the current directory/folder (./). As mentioned in part 1, we are performing alignment on reads that were trimmed at the 5'. Since we were looping over all the sequencing files, we used the ${describer} variable that changed with each iteration of the for-loop. You can replace this variable with the actual name of a file that needs to be aligned. The -S parameters is optional and is used to ask bowtie to generate the output aligned files in the SAM format. Before running the alignment, we created a directory named 'aligned,' and inserted the path to this folder in front of the -S parameter. This caused the aligner to save the output files inside the 'aligned' folder.
\r\n\r\n\r\nRead more about the SAM format: http://software.broadinstitute.org/software/igv/sam
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To create a directory using the terminal, you can run the make directory command (mkdir):
\r\nmkdir alignedAfter the alignment process is finished, you can visualize the SAM files using a visualization tool such as the Integrative Genomics Viewer (IGV) from the Broad Institute.
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Sort and normalize aligned reads
\r\nYou can get a lot of information from the aligned SAM files, and these may be sufficient for you. Alternatively, you may need to perform more complex analysis such as WT vs. mutant comparisons. For this purpose, it is necessary to perform some normalization to account for noise and technical variations that are introduced during ChIP-Seq procedure. This is an evolving field, and many alternative approaches are being used for normalizing ChIP-Seq data. We will go into more detail about the normalization methods in a later post, but, here, we give one example.
\r\nAn important step to enable normalization or visualization of the SAM files is to sort the aligned reads based on genomic location. To do this, we used SAM tools: https://anaconda.org/bioconda/samtools
\r\nAfter installing sam tools (see above link), we ran the following for-loop on the aligned sam files.
\r\nfor sample in *.sam; \r\ndo \r\necho $sample; \r\ndescriber=$(echo ${sample} | sed 's/.sam//'); \r\n/anaconda/bin/samtools view -bS ${describer}.sam | /anaconda/bin/samtools sort -o ${describer}.sorted.bam; \r\n/anaconda/bin/samtools index ${describer}.sorted.bam; \r\ndoneThe for-loop is similar to the previously described loops. Here we use the sort function of samtools to generate sorted bam files (BAM format). These bam files make it super easy to perform normalization or any other mathematical processing on the aligned reads. You can inspect the content of the SAM and BAM files using the head or tail function, as described in part 1.
\r\nAfter sorting the alignments, to perform normalization, we created a python script that uses the bedtools genomecov function: http://bedtools.readthedocs.io/en/latest/content/tools/genomecov.html
\r\nThis python script was generated because we needed to perform additional calculations before using the genomecov tool (see comments inside the python script file). You can directly use the genomecov tool if no additional preprocessing of the reads is required. Visit our GitHub page to view the python script file: https://github.com/elucidaid/public-bioinformatics/blob/master/normalize.py
\r\nTo use the normalize.py script, we performed the following command in terminal:
\r\n/anaconda/bin/python.app ./lab/scripts/normalize.py --bam sampleA_TCCGCGAA.sorted.bam --bw sampleA.bedgraphThe output of the normalization step is a file with the bedgraph format.
\r\n\r\n\r\nThe bedGraph format allows display of continuous-valued data in track format. This display type is useful for probability scores and transcriptome data. This track type is similar to the wiggle (WIG) format, but unlike the wiggle format, data exported in the bedGraph format are preserved in their original state.
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In part 3, we will discuss how to use R to plot the bedgraph files that were generated by the bedrolls genomecov function.
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Free sample scripts available on our GitHub page: https://github.com/elucidaid/public-bioinformatics
\r\nFor sample genomic data used in this turtorial click here.
\r\nWe do not have any affiliation to any of the tools mentioned in this blog post.
","postId":"5ad395bb-105a-59ed-b05e-4fc3b4a11f60","seo_title":"Bioinformatic analysis for biologists - part 2","meta_description":"In part 1, we described the steps involved in obtaining sequencing reads from ChIP-Seq libraries. We also described how to choose a analysis","previous":{"id":"4b494586-a4e0-563c-b297-552b54168d22","seo_title":"How to setup an electronic lab notebook","meta_description":"Learn how to set up an electronic lab notebook to improve workflow. Hint: electronic lab notebook free trial period is critical.","body":"Electronic Lab Notebooks (ELNs) are created to help science based laboratories keep track of data, files, notes, inventory items. Many additional time and money-saving features like team management and reporting may also be offered by some ELN options. Maybe you have just subscribed to a new Electronic Lab Notebook (ELN) service or maybe you're evaluating different options. Whatever the reason, it is important to be aware of the best practices when using an ELN system. It is important to note that the ELN market is a relatively young software market trying to meet the needs of a very diverse laboratory based activity ecosystem. For this reason there is no one set of standard guidelines to follow. There are however some things to consider when devising your own set of standards to maintain a high quality digital version of your research notebook.
\n\nAs biotech, pharmaceutical and academic research industries have become aware of the many benefits of Electronic Lab Notebook systems (ELN), the demand for ELN has grown substantially. Fortunately, in response, the number of available ELN software applications on the market has also increased to more than 60.
\nUnlike the general-purpose word processing and note-taking apps (e.g., MS Word, Evernote), each ELN app has a unique set of features that meet specific demands. You can see how a dedicated lab notebook app is different from a general note taking tool like Evernote by requesting a demo here.
\nTwo broad criteria that can define the suitability of a particular ELN system are the field of research (e.g. Microbiology vs Neuroscience) and the working environment (Academia vs Industry). For example, a scientist working in a chemistry lab and a research scientist working in a cell biology lab may find entirely different ELN software suitable for their research. Also, teams working in a research environment have a different set of requirements from those working in Quality Assurance/Quality Control (QA/QC). Click here to learn more about how LabLog™ supports R&D and QA/QC environments.
\nOther criteria that may define the suitability of an ELN can include:
\nThe availability of a vast variety of ELNs on the market makes it more likely for researchers to find a suitable solution. However, due to time constraints, it's hard to assess every single solution before settling on one. It is essential that you choose the right software from the outset of the transition from paper notebooks. For this purpose, it is important to discuss your requirements with a number of software vendors.
\nAt LabLog™, we have a lot of experience working with clients in biotech and pharmaceutical industries to understand their specific needs and provide a suitable electronic lab notebook free of charge. The purchasing process usually entails collaboration between the regulatory and scientific teams within the client organization. After discussions with the vendor, the client is allowed to test out the solution for a limited free trial period. At LabLog, depending on the size of the client organization, we may deploy an engineer to work with the clients on-site to setup the solution for the free trial period. If the individual organization decides to proceed with the purchasing, the next step will be vendor and software validation. The specifics of this stage will depend on client regulatory requirements. LabLog has a team of in-house regulatory affairs personnel that can work with clients during the validation process. Additionally, audit reports and other relevant documents will be shared with individual clients. A brief overview of these procedures can be discussed during the initial product demo.
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We cannot emphasize enough the importance of choosing the right ELN from the outset. This is because each digital notebook will have its characteristics and flexibility concerning layout and setup. For example, the simple user interface of LabLog™ mimics the design of a word-processing software so that it is familiar to the user and does not require any training to get started immediately. Whatever your final choice of ELN, we recommend following the steps below to set yourself up for success when transitioning from paper notebooks:
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Conclusion
\nChoose the right ELN, be creative, have fun, and let the ELN do all the heavy lifting for you. You can always rearrange electronic notes, and use the search function to find anything you need with just one click.
\n\n\n","title":"How to set up an Electronic Lab Notebook","slug":"how-to-setup-an-electronic-lab-notebook"},"next":{"id":"4f2461f9-f536-5d0e-9a7f-1e5d8a732a89","seo_title":"12 Best Cell Biology Bloggers You Need to Follow","meta_description":"Recently, at a dinner gathering with a few of my scientist friends, I was asked what I think is the best cell biology blog. Here's my pick!","body":"
Recently, at a dinner gathering with a few of my scientist friends, I was asked what I think is the best cell biology blog. It’s hard to tell because most cell biology blogs have their own specific topic or their own “voice”. While I do not have one favorite blog, there are few I read on a regular basis.
\r\nThe following is my selection of what I think are top cell biology blogs. Unfortunately, smaller blog sites have a high turnover rate, and I will try to keep this list updated to the best of my ability. If you notice a broken link, leave a comment on our facebook page. Suggestions will be appreciated.
\r\nDiary of a cancer cell: Read about the science behind cancer from the unique point of view of a little cancer cell, who doesn't want to be bad. Accurate science and humor, a light read for anyone who wishes to understand the disease a little better.
\r\nNIH Biomedical Beat Blog: This blog is from the National Institute of General Medical Sciences (NIGMS). It publishes short articles about research and scientists as well as images that help illustrate important biomedical concepts and advances.
\r\nLittle Cell Biology Blog: Science-related articles that appeal to a wide audience. You can ask questions and post your own articles.
\r\nPLOS Biologue: Blog posts cover the latest research and discussions from open access publishing developments and science policy, to science outreach and education, and the implications of new discoveries in biological research.
\r\nThe Niche: Dr. Paul Knoepfler (@pknoepfler) is a biomedical scientist, science writer, advocate, and cancer survivor. This blog is maitained by his team.
\r\nDELMIC Microscopy Blog: DELMIC B.V. is a company based in the Netherlands that produces correlative light and electron microscopy solutions. This blog covers the applications and techniques in the fields of correlative light and electron microscopy.
\r\nAddgene: Addgene is a non-profit service provider for life science research. Addgene's blog provides a platform to share information.
\r\n\r\nThe Biology Blog: Molecular and Cell Biology new, articles and tips. This blog publishes focused news, articles, and tips for both professional and non-professional readers.
\r\niBiology: High-quality videos describing exciting topics in biology, from basic techniques to important discoveries.
\r\nJung's Biology Blog: One of the longest running personal blogs I have come across. Interesting posts from a professor's point of view.
\r\nRoyal Society of Biology blog: Short posts discussing the current topics in biology-related fields and community outreach activities.
\r\nPeerJ Blog: Stimulating blog posts discuss recent findings in biology and related fields.
\r\nPro tip: Perform a search on Medium.com to read the latest blog posts about biology from a collection of bloggers on this platform.
\r\n\r\n\r\nMedium taps into the brains of the world’s most insightful writers, thinkers, and storytellers to bring you the smartest takes on topics that matter. So whatever your interest, you can always find fresh thinking and unique perspectives.
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Here are some related results on Medium: https://medium.com/search?q=biology
\r\nHere's a sample of a personal blog related to biology: https://medium.com/@scienceguy
\r\nWhat’s your favorite cell biology blog and why? Share on our Facebook page: https://www.facebook.com/LabNotebookApp
\r\nThis blog is run by LabLog: https://labnotebook.app
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