{"id":7128,"date":"2023-09-19T15:30:23","date_gmt":"2023-09-19T19:30:23","guid":{"rendered":"https:\/\/ptp.cloud\/?p=7128"},"modified":"2025-06-29T20:25:52","modified_gmt":"2025-06-30T00:25:52","slug":"bioinformatics-pipelines","status":"publish","type":"post","link":"https:\/\/ptp.cloud\/bioinformatics-pipelines\/","title":{"rendered":"Bioinformatics Pipeline Automation and Optimization via AWS and PTP"},"content":{"rendered":"[et_pb_section fb_built=&#8221;1&#8243; admin_label=&#8221;section&#8221; _builder_version=&#8221;4.16&#8243; da_disable_devices=&#8221;off|off|off&#8221; global_colors_info=&#8221;{}&#8221; da_is_popup=&#8221;off&#8221; da_exit_intent=&#8221;off&#8221; da_has_close=&#8221;on&#8221; da_alt_close=&#8221;off&#8221; da_dark_close=&#8221;off&#8221; da_not_modal=&#8221;on&#8221; da_is_singular=&#8221;off&#8221; da_with_loader=&#8221;off&#8221; da_has_shadow=&#8221;on&#8221;][et_pb_row admin_label=&#8221;row&#8221; _builder_version=&#8221;4.16&#8243; background_size=&#8221;initial&#8221; background_position=&#8221;top_left&#8221; background_repeat=&#8221;repeat&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;|||&#8221; global_colors_info=&#8221;{}&#8221; custom_padding__hover=&#8221;|||&#8221;][et_pb_code _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]\n<article><!-- [et_pb_line_break_holder] -->  <pee>Are your bioinformatics pipelines slow, crashing, or hard to scale? In this video, Scott Schreirey from PTP breaks down how to streamline and optimize bioinformatics workflows using AWS features like Batch, S3, and SageMaker.<\/pee><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] -->  <\/p>\n<div style=\"position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden;\"><!-- [et_pb_line_break_holder] -->    <iframe style=\"position: absolute; top: 0; left: 0; width: 100%; height: 100%;\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/dd4JbGaF2DM?rel=0\" title=\"How PTP Optimizes and Automates Bioinformatics Pipelines \u2013 AWS &#038; Life Sciences\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen loading=\"lazy\" referrerpolicy=\"strict-origin-when-cross-origin\"><\/iframe><!-- [et_pb_line_break_holder] -->  <\/div>\n<p><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] -->  <pee style=\"text-align: center;\"><!-- [et_pb_line_break_holder] -->    <a href=\"https:\/\/www.youtube.com\/watch?v=dd4JbGaF2DM\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"Watch the full video on YouTube\" style=\"text-decoration: none;\"><!-- [et_pb_line_break_holder] -->      <strong>Watch the full video on YouTube<\/strong><img src=\"https:\/\/ptp.cloud\/wp-content\/uploads\/2024\/09\/Youtube-icon-300x300.png\" alt=\"YouTube logo for PTP bioinformatics pipelines optimization video\" width=\"26\" height=\"26\" loading=\"lazy\" decoding=\"async\" style=\"vertical-align: middle; margin-left: 8px;\" \/><!-- [et_pb_line_break_holder] -->    <\/a><!-- [et_pb_line_break_holder] -->  <\/pee><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] -->  <\/p>\n<h2>Problem: Is Your Pipeline Inefficient, Slow, or Keeps Crashing?<\/h2>\n<p><!-- [et_pb_line_break_holder] -->  <pee>As a computational biologist, you\u2019re likely working with sequencing platforms like Illumina, PacBio, 10x Genomics, or Vizgen\u2014and your pipelines process massive volumes of data from FastQ, H5AD, or VCF files. But as research scales and instruments evolve, those pipelines can become bottlenecks.<\/pee><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] -->  <pee>You might have a pipeline that works&#8230; most of the time. But it\u2019s slow, or unreliable, or hard to automate. As you approach critical milestones\u2014like funding rounds or clinical trial validation\u2014these inefficiencies cost time and opportunity. Scaling and parallelizing pipelines within <a href=\"https:\/\/aws.amazon.com\/\" target=\"_blank\" rel=\"noopener noreferrer\">AWS<\/a> can eliminate these challenges.<\/pee><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] -->  <\/p>\n<h2>AWS Features That Accelerate Your Workflows<\/h2>\n<p><!-- [et_pb_line_break_holder] -->  <pee>Nextflow and Airflow are powerful tools for managing workflows, especially when combined with <a href=\"https:\/\/aws.amazon.com\/batch\/\" target=\"_blank\" rel=\"noopener noreferrer\">AWS Batch<\/a>, which automates parallel job processing. These jobs can be triggered automatically when new data is generated, using scalable infrastructure configured with optimized compute instances.<\/pee><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] -->  <pee>Once processed, data is stored in Amazon <a href=\"https:\/\/aws.amazon.com\/s3\/\" target=\"_blank\" rel=\"noopener noreferrer\">S3<\/a> in a usable format\u2014whether that\u2019s for visualization or structured formats like JSON used to train machine learning models in <a href=\"https:\/\/aws.amazon.com\/sagemaker\/\" target=\"_blank\" rel=\"noopener noreferrer\">SageMaker<\/a>.<\/pee><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] -->  <pee>These improvements aren\u2019t just about performance. In many cases, pipeline processing time has been reduced by over 70%, while also decreasing cloud spend\u2014thanks to more efficient automation and job orchestration.<\/pee><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] -->  <pee style=\"text-align: center;\"><!-- [et_pb_line_break_holder] -->    <a href=\"https:\/\/aws.amazon.com\/marketplace\/pp\/prodview-it7fjq6rqix74?sr=0-13&#038;ref_=beagle&#038;applicationId=AWSMPContessa\" target=\"_blank\" rel=\"noopener noreferrer\"><!-- [et_pb_line_break_holder] -->      <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/ptp.cloud\/wp-content\/uploads\/2024\/04\/aws-marketplace-logo-1-300x49.png\" alt=\"AWS Marketplace logo for CloudOps for Life Sciences Startups\" width=\"300\" height=\"49\" style=\"display: block; margin-left: auto; margin-right: auto;\" \/><!-- [et_pb_line_break_holder] -->    <\/a><!-- [et_pb_line_break_holder] -->  <\/pee><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] -->  <pee style=\"text-align: center;\"><!-- [et_pb_line_break_holder] -->    <a href=\"https:\/\/aws.amazon.com\/marketplace\/pp\/prodview-it7fjq6rqix74?sr=0-13&#038;ref_=beagle&#038;applicationId=AWSMPContessa\" target=\"_blank\" rel=\"noopener noreferrer\"><!-- [et_pb_line_break_holder] -->      <strong>If you\u2019re interested, check out PTP CloudOps for Life Sciences Startups on AWS Marketplace<\/strong><!-- [et_pb_line_break_holder] -->    <\/a><!-- [et_pb_line_break_holder] -->  <\/pee><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] -->  <pee>Need help scaling your genomics pipelines? Learn how our <a href=\"\/services\/cloudops\/cloud-engineering\/\" title=\"scientific computing IT support\">scientific computing IT support<\/a> helps research teams automate, scale, and accelerate breakthroughs in life sciences.<\/pee><!-- [et_pb_line_break_holder] --><\/article>\n<p><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><script type=\"application\/ld+json\"><!-- [et_pb_line_break_holder] -->{<!-- [et_pb_line_break_holder] -->  \"@context\": \"https:\/\/schema.org\",<!-- [et_pb_line_break_holder] -->  \"@type\": \"VideoObject\",<!-- [et_pb_line_break_holder] -->  \"name\": \"How PTP Optimizes and Automates Bioinformatics Pipelines \u2013 AWS & Life Sciences\",<!-- [et_pb_line_break_holder] -->  \"description\": \"Learn how PTP uses AWS services like Batch, S3, and SageMaker to optimize and automate bioinformatics pipelines. 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He highlights the use of AWS Batch, Nextflow, and Airflow to enhance pipeline efficiency, reliability, and speed. Scheirey explains how these tools can help process large volumes of genomic data more quickly and cost-effectively, ultimately supporting research validation and clinical trials.<\/p>\n","protected":false},"author":2,"featured_media":12808,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"[embed]https:\/\/youtu.be\/dd4JbGaF2DM[\/embed]\r\n\r\n????: https:\/\/myermedia.co\/\r\n\r\n\u00a0\r\n\r\n<strong>Problem: Is your Pipeline inefficient, slow, or keeps crashing?<\/strong>\r\n\r\nAs a Computational biologist, you\u2019re working on bioinformatics workflows and utilizing different sequencers - Illumina, PacBio, 10x Genomics or Vizgen to name a few, or the vast array of spectrometry and imaging instruments available, and the data are all being processed by your various Pipelines.\r\n\r\nYou may have a pipeline that works, works most of the time, or was working for you even though it might have taken hours if not days to run. You\u2019re questioning whether this could be faster, more reliable, or how easy it would be to automate. (Data generation \/ Storage of Data)\r\n\r\nOften times it\u2019s when you are generating more Genomics or Bulk RNAseq data, for example, FastQ files, H5AD, VCF Files are growing at a rapid pace, due to new instrument or research initiative, and you need to increase the speed and efficiency to scale your pipelines. Typically because you\u2019re looking to achieve your next-level funding or clinical trials and validating your research is the only way to keep your company moving forward.\r\n\r\nThere are ways you can split your pipeline into parallel processes and run them simultaneously within AWS that data science folks may not be familiar with.\r\n\r\nAWS New features\r\n\r\nCertain pipelines can be optimized by using Nextflow, or Airflow, whether they are home-grown or built with existing tools, and utilizing AWS Batch to run multiple jobs to process these files at the same time.\r\n\r\nUltimately using optimized AWS compute instances, automatically auto-scaling and running jobs autonomously from when data is generated by an instrument, and ultimately stored in its \u2018converted\u2019 format in S3.\r\n\r\nThis isn\u2019t only for human readable data that you\u2019d examine in visualizations, but also JSON files, for example ones that illustrate gene expression levels with gene labels, that will ultimately populate AI \/ ML models in Sagemaker.\r\n\r\nI\u2019ve seen companies reduce processing time by over 70 percent, and counter-intuitively this may also save your company a lot of money in your cloud expenses.\r\n\r\n<a href=\"https:\/\/aws.amazon.com\/marketplace\/pp\/prodview-it7fjq6rqix74?sr=0-13&ref_=beagle&applicationId=AWSMPContessa\"><strong>If you\u2019re interested in learning more PTP CloudOps for Life Sciences Startups on the AWS Marketplace<\/strong><\/a>\r\n\r\n<a href=\"https:\/\/ptp.cloud\/event-calendar\/\"><strong>Check out all of PTP's Life Sciences \/ AWS Events<\/strong><\/a>","_et_gb_content_width":"","content-type":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[23,12,9,180,83],"tags":[163,162,161],"table_tags":[],"class_list":["post-7128","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-aws-archive","category-aws-for-life-sciences-archive","category-cloudops-archive","category-devops-archive","category-learning-videos-archive","tag-bioinformatics-pipeline","tag-bioinformatics-workflow","tag-computational-biology"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.1.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Automating and Optimizing Bioinformatics Pipelines<\/title>\n<meta name=\"description\" content=\"As a Computational biologist, you\u2019re working on 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Cloud Experts | Biotech Enablers\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/PTPCloud\" \/>\n<meta property=\"article:published_time\" content=\"2023-09-19T19:30:23+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-06-30T00:25:52+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/ptp.cloud\/wp-content\/uploads\/2024\/06\/Scott-Scheirey-Pipeline-Optimization-for-Computational-Biologists.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2056\" \/>\n\t<meta property=\"og:image:height\" content=\"1380\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Gary Derheim\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@PTPCloud\" \/>\n<meta name=\"twitter:site\" content=\"@PTPCloud\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Gary Derheim\" 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