{"id":14431,"date":"2022-05-11T00:23:34","date_gmt":"2022-05-11T04:23:34","guid":{"rendered":"https:\/\/ptp.cloud\/?p=14431"},"modified":"2025-08-12T03:45:23","modified_gmt":"2025-08-12T07:45:23","slug":"apt-reduces-pipeline-runtime-with-aws-batch-nextflow","status":"publish","type":"post","link":"https:\/\/ptp.cloud\/apt-reduces-pipeline-runtime-with-aws-batch-nextflow\/","title":{"rendered":"How Adaptive Phage Therapeutics Streamlined Data Pipelines with AWS Batch and Nextflow"},"content":{"rendered":"[et_pb_section fb_built=&#8221;1&#8243; custom_padding_last_edited=&#8221;on|tablet&#8221; admin_label=&#8221;Section&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#3e489d&#8221; background_image=&#8221;https:\/\/ptp.cloud\/wp-content\/uploads\/2024\/10\/Square-Pattern-Hero-Background.png&#8221; custom_padding=&#8221;5px||||false|false&#8221; custom_padding_tablet=&#8221;40px||40px||true|false&#8221; custom_padding_phone=&#8221;40px||40px||true|false&#8221; da_disable_devices=&#8221;off|off|off&#8221; locked=&#8221;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 column_structure=&#8221;2_3,1_3&#8243; make_equal=&#8221;on&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; width=&#8221;85%&#8221; max_width=&#8221;1380px&#8221; custom_padding=&#8221;63px||1px|||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;2_3&#8243; module_class=&#8221;col-vert-cent&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_heading title=&#8221;How Adaptive Phage Therapeutics Reduced Pipeline Runtime by 80% with AWS Batch and Nextflow*&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; title_font=&#8221;&#8211;et_global_heading_font|700|||||||&#8221; title_text_color=&#8221;#ffffff&#8221; title_font_size=&#8221;3.5rem&#8221; title_line_height=&#8221;1.2em&#8221; max_width_tablet=&#8221;620px&#8221; max_width_phone=&#8221;620px&#8221; max_width_last_edited=&#8221;on|tablet&#8221; custom_padding=&#8221;40px||||false|false&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_heading][\/et_pb_column][et_pb_column type=&#8221;1_3&#8243; module_class=&#8221;col-vert-cent&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_image src=&#8221;https:\/\/ptp.cloud\/wp-content\/uploads\/2024\/10\/PTP-Hero-Section-Goat-Illustration-min.png&#8221; alt=&#8221;Illustration of Goat working on servers leading data to the cloud and to a proved treatment&#8221; title_text=&#8221;PTP Hero Section Goat Illustration&#8221; align=&#8221;center&#8221; disabled_on=&#8221;on|off|off&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; max_width_tablet=&#8221;75%&#8221; max_width_phone=&#8221;75%&#8221; max_width_last_edited=&#8221;on|tablet&#8221; custom_padding=&#8221;25px||25px||true|false&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row make_equal=&#8221;on&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; width=&#8221;85%&#8221; max_width=&#8221;1380px&#8221; custom_margin=&#8221;|auto|29px|auto||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; module_class=&#8221;col-vert-cent&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;&#8211;et_global_heading_font|300|||||||&#8221; text_text_color=&#8221;#ffffff&#8221; text_font_size=&#8221;1.5rem&#8221; text_line_height=&#8221;2.1rem&#8221; max_width_tablet=&#8221;620px&#8221; max_width_phone=&#8221;620px&#8221; max_width_last_edited=&#8221;on|tablet&#8221; global_colors_info=&#8221;{}&#8221;]\n<p data-pm-slice=\"1 1 []\"><span>Adaptive Phage Therapeutics (APT) is a clinical-stage biotech company advancing therapies to combat multi-drug-resistant infections. Traditional antimicrobials are becoming obsolete as pathogens evolve resistance. APT\u2019s innovative PhageBank therapy leverages a growing library of phages, offering evergreen broad-spectrum and polymicrobial coverage. Through a proprietary phage susceptibility assay, commercialized with Mayo Clinic Laboratories, APT is positioned to revolutionize antimicrobial treatment on a global scale. This groundbreaking work is partially funded by the U.S. Department of Defense.<\/span><\/p>\n[\/et_pb_text][dsm_icon_list icon_font_size_tablet=&#8221;&#8221; icon_font_size_phone=&#8221;20px&#8221; icon_font_size_last_edited=&#8221;on|phone&#8221; module_class=&#8221;hero-stat&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font_size_tablet=&#8221;&#8221; text_font_size_phone=&#8221;13px&#8221; text_font_size_last_edited=&#8221;on|phone&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;][dsm_icon_list_child text=&#8221;*Decreased from 5-6 hours to just 1.15 hours, an approximate 80% improvement.&#8221; font_icon=&#8221;&#xf021;||fa||900&#8243; icon_padding=&#8221;1px&#8221; icon_font_size=&#8221;2rem&#8221; url_new_window=&#8221;on&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;&#8211;et_global_heading_font|500|on||||||&#8221; text_text_color=&#8221;#0ca8fc&#8221; text_font_size=&#8221;1.2rem&#8221; text_line_height=&#8221;1.25rem&#8221; text_font_size_tablet=&#8221;1.2rem&#8221; text_font_size_phone=&#8221;1rem&#8221; text_font_size_last_edited=&#8221;on|phone&#8221; global_colors_info=&#8221;{}&#8221;][\/dsm_icon_list_child][\/dsm_icon_list][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; admin_label=&#8221;section&#8221; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;32px||1px|||&#8221; da_disable_devices=&#8221;off|off|off&#8221; locked=&#8221;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 column_structure=&#8221;1_2,1_2&#8243; make_equal=&#8221;on&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; width=&#8221;85%&#8221; max_width=&#8221;1380px&#8221; custom_padding=&#8221;8px||1px|||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;1_2&#8243; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;&#8211;et_global_heading_font||||||||&#8221; text_text_color=&#8221;gcid-secondary-color&#8221; text_font_size=&#8221;28px&#8221; text_line_height=&#8221;2rem&#8221; custom_margin=&#8221;45px||||false|false&#8221; custom_padding=&#8221;||||false|false&#8221; text_font_size_tablet=&#8221;28px&#8221; text_font_size_phone=&#8221;24px&#8221; text_font_size_last_edited=&#8221;on|phone&#8221; global_colors_info=&#8221;{%22gcid-secondary-color%22:%91%22text_text_color%22%93}&#8221;]\n<h2><span style=\"font-size: xx-large; color: #0c71c3;\"><strong>The Challenge<\/strong><\/span><\/h2>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=&#8221;1_2&#8243; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_image src=&#8221;https:\/\/ptp.cloud\/wp-content\/uploads\/2025\/01\/adaptive-phage-therapeutics-logo.png&#8221; alt=&#8221;Logo of Adaptive Phage Therapeutics featuring abstract blue phage shapes and company name in stylish typography.&#8221; title_text=&#8221;adaptive-phage-therapeutics-logo&#8221; url=&#8221;https:\/\/www.linkedin.com\/company\/adaptive-phage-therapeutics&#8221; url_new_window=&#8221;on&#8221; align=&#8221;right&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row make_equal=&#8221;on&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; width=&#8221;85%&#8221; max_width=&#8221;1380px&#8221; custom_padding=&#8221;19px||63px|||&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; module_class=&#8221;col-vert-cent&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;&#8211;et_global_heading_font|300|||||||&#8221; text_text_color=&#8221;gcid-secondary-color&#8221; text_font_size=&#8221;28px&#8221; text_line_height=&#8221;2.3rem&#8221; custom_margin=&#8221;||7px|||&#8221; text_font_size_tablet=&#8221;28px&#8221; text_font_size_phone=&#8221;24px&#8221; text_font_size_last_edited=&#8221;on|phone&#8221; global_colors_info=&#8221;{%22gcid-secondary-color%22:%91%22text_text_color%22,%22text_text_color%22,%22text_text_color%22%93}&#8221;]\n<p><span style=\"color: rgba(0, 0, 0, 0.72);\"><a href=\"https:\/\/www.biomx.com\/\">APT<\/a> had an existing pipeline built with Snakemake, a bioinformatics workflow engine. While the team\u2019s cloud environment was effective, they sought to standardize to a High-Performance Compute (HPC) platform that could:<\/span><\/p>\n<p><span style=\"color: rgba(0, 0, 0, 0.72);\">\u2022\u00a0 Accelerate pipeline runtimes to deliver refined data faster.<\/span><\/p>\n<p><span style=\"color: rgba(0, 0, 0, 0.72);\">\u2022\u00a0 Enable scientists to process data efficiently, reducing pipeline execution times from days to minutes where possible.<\/span><\/p>\n<p><span style=\"color: rgba(0, 0, 0, 0.72);\">\u2022\u00a0 Scale dynamically to meet variable computational demands while managing costs.<\/span><\/p>\n<p><span style=\"color: rgba(0, 0, 0, 0.72);\">Pipeline jobs, originating from wet bench labs, required scientists to submit raw data through the pipeline. The speed of processing directly impacted the pace of research, making optimization critical.<\/span><\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; custom_padding_last_edited=&#8221;on|tablet&#8221; next_background_color=&#8221;#ffffff&#8221; admin_label=&#8221;Section&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#3e489d&#8221; background_image=&#8221;https:\/\/ptp.cloud\/wp-content\/uploads\/2024\/10\/Square-Pattern-Hero-Background.png&#8221; custom_padding=&#8221;||52px||false|false&#8221; custom_padding_tablet=&#8221;40px||40px||true|false&#8221; custom_padding_phone=&#8221;40px||40px||true|false&#8221; bottom_divider_style=&#8221;arrow&#8221; bottom_divider_height=&#8221;83px&#8221; da_disable_devices=&#8221;off|off|off&#8221; locked=&#8221;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 make_equal=&#8221;on&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; width=&#8221;85%&#8221; max_width=&#8221;1380px&#8221; custom_padding=&#8221;||99px|||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; module_class=&#8221;col-vert-cent&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;&#8211;et_global_heading_font|300|||||||&#8221; text_text_color=&#8221;gcid-secondary-color&#8221; text_font_size=&#8221;28px&#8221; text_line_height=&#8221;2.3rem&#8221; custom_margin=&#8221;||0px|||&#8221; text_font_size_tablet=&#8221;28px&#8221; text_font_size_phone=&#8221;24px&#8221; text_font_size_last_edited=&#8221;on|phone&#8221; global_colors_info=&#8221;{%22gcid-secondary-color%22:%91%22text_text_color%22,%22text_text_color%22%93}&#8221;]\n<p><strong style=\"color: #ffffff; font-size: xx-large; font-family: Ubuntu, Helvetica, Arial, Lucida, sans-serif;\">The Solution<\/strong><\/p>\n<p><a href=\"https:\/\/aws.amazon.com\/codecommit\/\" style=\"font-size: 28px; font-family: var(--et_global_heading_font),serif;\">PTP\u2019s CloudOps Engineering team<\/a><span style=\"color: #ffffff; font-size: 28px; font-family: var(--et_global_heading_font),serif;\"> designed a modernized architecture to update APT\u2019s pipeline discovery process, leveraging AWS Batch and migrating from Snakemake to <\/span><a href=\"https:\/\/www.nextflow.io\/\" style=\"font-size: 28px; font-family: var(--et_global_heading_font),serif;\">Nextflow<\/a><span style=\"color: #ffffff; font-size: 28px; font-family: var(--et_global_heading_font),serif;\">:<\/span><\/p>\n<p><strong><span style=\"color: #ffffff;\">AWS Batch and Nextflow Integration<br \/><\/span><\/strong><span style=\"color: #ffffff; font-size: 28px; font-family: var(--et_global_heading_font),serif;\">\u2022\u00a0 Pipelines now begin with a job submitted to AWS Batch, which launches an EC2 instance running Nextflow.<\/span><\/p>\n<p><span style=\"color: #ffffff;\">\u2022\u00a0 Docker images handle pipeline execution, ensuring standardization and repeatability.<\/span><\/p>\n<p><span style=\"color: #ffffff;\">\u2022\u00a0 Nextflow dynamically provisions instances for parallel processing, improving efficiency and reducing costs.<\/span><\/p>\n<p><strong><span style=\"color: #ffffff;\">Automated Workflow with Lambda<br \/><\/span><\/strong><span style=\"color: #ffffff; font-size: 28px; font-family: var(--et_global_heading_font),serif;\">\u2022\u00a0 Lambda functions monitor S3 buckets for new data uploads from wet labs and automatically trigger pipeline execution.<\/span><\/p>\n<p><span style=\"color: #ffffff;\">\u2022\u00a0 Outputs from the pipeline are saved to S3, triggering additional Lambda functions to initiate subsequent pipelines.<\/span><\/p>\n<p><strong><span style=\"color: #ffffff;\">Optimized Data Storage and Organization<br \/><\/span><\/strong><span style=\"color: #ffffff; font-size: 28px; font-family: var(--et_global_heading_font),serif;\">\u2022\u00a0 All pipeline data is stored and organized in S3, with outputs arranged for downstream analysis.<\/span><\/p>\n<p><span style=\"color: #ffffff;\">\u2022\u00a0 Polished data is processed through additional pipelines as needed, with results consolidated and presented to scientists.<\/span><span style=\"font-size: 28px; font-family: var(--et_global_heading_font),serif;\">\u00a0<\/span><\/p>\n[\/et_pb_text][et_pb_image src=&#8221;https:\/\/ptp.cloud\/wp-content\/uploads\/2024\/05\/aws_infrastructure_design_nextflow_workflow.jpg&#8221; alt=&#8221;A diagram illustrating a Nextflow workflow using AWS infrastructure, including workflow-queue, job-queue, Nextflow head-node, Nextflow processes, and S3 buckets for workflow scripts and input\/output files.&#8221; title_text=&#8221;aws_infrastructure_design_nextflow_workflow&#8221; align=&#8221;center&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||144px|||&#8221; custom_padding=&#8221;137px|||||&#8221; box_shadow_style=&#8221;preset1&#8243; box_shadow_blur=&#8221;22px&#8221; box_shadow_spread=&#8221;27px&#8221; box_shadow_color=&#8221;#FFFFFF&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_image][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;&#8211;et_global_heading_font|300|||||||&#8221; text_text_color=&#8221;gcid-secondary-color&#8221; text_font_size=&#8221;28px&#8221; text_line_height=&#8221;2.3rem&#8221; custom_margin=&#8221;||0px|||&#8221; text_font_size_tablet=&#8221;28px&#8221; text_font_size_phone=&#8221;24px&#8221; text_font_size_last_edited=&#8221;on|phone&#8221; global_colors_info=&#8221;{%22gcid-secondary-color%22:%91%22text_text_color%22,%22text_text_color%22%93}&#8221;]\n<p><strong style=\"font-size: 28px; font-family: var(--et_global_heading_font),serif;\"><span style=\"color: #ffffff;\">AWS Services Implemented:<\/span><\/strong><span style=\"color: #ffffff;\"><\/span><\/p>\n<p><strong> <\/strong><\/p>\n<p><strong><\/strong><strong><span style=\"color: #ffffff;\"><\/span><\/strong><strong style=\"color: #ffffff; font-size: 28px; font-family: var(--et_global_heading_font),serif;\">Compute and Storage<br \/><\/strong><a href=\"https:\/\/aws.amazon.com\/pm\/ec2\/\" style=\"font-size: 28px; font-family: var(--et_global_heading_font),serif;\">EC2<\/a><span style=\"color: #ffffff; font-size: 28px; font-family: var(--et_global_heading_font),serif;\">, <\/span><a href=\"https:\/\/aws.amazon.com\/batch\/\" style=\"font-size: 28px; font-family: var(--et_global_heading_font),serif;\">AWS Batch<\/a><span style=\"color: #ffffff; font-size: 28px; font-family: var(--et_global_heading_font),serif;\">, <\/span><a href=\"https:\/\/aws.amazon.com\/lambda\/\" style=\"font-size: 28px; font-family: var(--et_global_heading_font),serif;\">Lambda<\/a><span style=\"color: #ffffff; font-size: 28px; font-family: var(--et_global_heading_font),serif;\">, <\/span><a href=\"https:\/\/aws.amazon.com\/ecr\/\" style=\"font-size: 28px; font-family: var(--et_global_heading_font),serif;\">ECR<\/a><span style=\"color: #ffffff; font-size: 28px; font-family: var(--et_global_heading_font),serif;\">, and <\/span><a href=\"https:\/\/aws.amazon.com\/pm\/serv-s3\/\" style=\"font-size: 28px; font-family: var(--et_global_heading_font),serif;\">S3<\/a><\/p>\n<p><span style=\"color: #ffffff;\"><strong>Governance and Monitoring<br \/><\/strong><a href=\"https:\/\/aws.amazon.com\/codecommit\/\">CodeCommit<\/a>, <a href=\"https:\/\/aws.amazon.com\/cloudformation\/\">CloudFormation<\/a>, <a href=\"https:\/\/aws.amazon.com\/cloudwatch\/\">CloudWatch<\/a>, <a href=\"https:\/\/aws.amazon.com\/cloudtrail\/\">CloudTrail<\/a>, <a href=\"https:\/\/aws.amazon.com\/iam\/\">IAM<\/a>, and <a href=\"https:\/\/aws.amazon.com\/config\/\">AWS Config<\/a><\/span><\/p>\n<p><span style=\"color: #ffffff;\"><strong>Networking<br \/><\/strong><a href=\"https:\/\/aws.amazon.com\/vpc\/\">Virtual Private Cloud (VPC)<\/a><\/span><\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; admin_label=&#8221;section&#8221; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;32px||1px|||&#8221; da_disable_devices=&#8221;off|off|off&#8221; locked=&#8221;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 make_equal=&#8221;on&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; width=&#8221;85%&#8221; max_width=&#8221;1380px&#8221; custom_margin=&#8221;36px|auto||auto||&#8221; custom_padding=&#8221;2px||42px|||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;&#8211;et_global_heading_font|300|||||||&#8221; text_text_color=&#8221;gcid-secondary-color&#8221; text_font_size=&#8221;28px&#8221; text_line_height=&#8221;2rem&#8221; custom_margin=&#8221;||3px|||&#8221; custom_padding=&#8221;||11px|||&#8221; text_font_size_tablet=&#8221;28px&#8221; text_font_size_phone=&#8221;24px&#8221; text_font_size_last_edited=&#8221;on|phone&#8221; global_colors_info=&#8221;{%22gcid-secondary-color%22:%91%22text_text_color%22%93}&#8221;]\n<h2><span style=\"font-size: xx-large; color: #0c71c3;\"><strong>The Outcome<\/strong><\/span><span style=\"font-size: xx-large; color: #0c71c3;\"><strong><\/strong><\/span><\/h2>\n<p><span style=\"font-size: xx-large; color: #0c71c3;\"><strong><\/strong><\/span><\/p>\n<p><span style=\"color: rgba(0, 0, 0, 0.87);\">The implementation of AWS Batch and Nextflow delivered transformative results for APT:<\/span><\/p>\n<p><span style=\"font-size: xx-large; color: #0c71c3;\"><span style=\"color: rgba(0, 0, 0, 0.87);\"><\/span><\/span><\/p>\n[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;&#8211;et_global_heading_font|300|||||||&#8221; text_text_color=&#8221;rgba(0,0,0,0.66)&#8221; text_font_size=&#8221;28px&#8221; text_line_height=&#8221;2.3rem&#8221; custom_margin=&#8221;||41px|||&#8221; custom_padding=&#8221;33px|25px|33px|25px|true|true&#8221; text_font_size_tablet=&#8221;28px&#8221; text_font_size_phone=&#8221;24px&#8221; text_font_size_last_edited=&#8221;on|phone&#8221; border_radii=&#8221;on|2px|2px|2px|2px&#8221; border_width_all=&#8221;1px&#8221; global_colors_info=&#8221;{%22gcid-secondary-color%22:%91%22text_text_color%22,%22text_text_color%22,%22text_text_color%22,%22text_text_color%22%93}&#8221;]\n<p><span style=\"color: rgba(0, 0, 0, 0.87);\"><strong>Runtime Reduction<br \/><\/strong>Pipeline <span style=\"color: #0c71c3;\"><em><strong>runtimes decreased from 5-6 hours to just 1.15 hours<\/strong><\/em> <\/span>using approximately 650 CPUs.<\/span><\/p>\n<p><span style=\"color: rgba(0, 0, 0, 0.87);\"><strong>Parallel Processing<br \/><\/strong>Leveraging AWS Batch allowed pipelines to run concurrently, delivering actionable data faster without additional costs.<\/span><\/p>\n<p><span style=\"color: rgba(0, 0, 0, 0.87);\"><strong>Enhanced Scalability<br \/><\/strong>The architecture enables APT to handle increasing data volumes efficiently.<\/span><\/p>\n<p><span style=\"color: rgba(0, 0, 0, 0.87);\"><strong>Extended Team Support<br \/><\/strong>PTP\u2019s CloudOps Engineering team provides ongoing design, architecture, and cloud management support, acting as an extension of APT\u2019s data science team.<\/span><\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=&#8221;1_2,1_2&#8243; 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global_colors_info=&#8221;{}&#8221;][\/et_pb_image][et_pb_heading title=&#8221;Ready to optimize your HPC environment? &#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; title_level=&#8221;h2&#8243; title_font=&#8221;&#8211;et_global_heading_font|600|||||||&#8221; title_text_align=&#8221;left&#8221; title_text_color=&#8221;gcid-0e52a8f4-5dfd-4102-9f40-fd354ef9edd5&#8243; title_font_size=&#8221;2.88rem&#8221; title_line_height=&#8221;1.2em&#8221; max_width_tablet=&#8221;100%&#8221; max_width_phone=&#8221;300px&#8221; max_width_last_edited=&#8221;on|phone&#8221; custom_margin=&#8221;15px||5px||false|false&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{%22gcid-0e52a8f4-5dfd-4102-9f40-fd354ef9edd5%22:%91%22title_text_color%22%93}&#8221;][\/et_pb_heading][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;Ubuntu||||||||&#8221; text_text_color=&#8221;#000000&#8243; text_font_size=&#8221;1.25rem&#8221; text_line_height=&#8221;1.75rem&#8221; max_width=&#8221;620px&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;]\n<p><span style=\"color: rgba(0, 0, 0, 0.84);\">APT\u2019s transition to an AWS Batch and Nextflow-based pipeline architecture underscores the transformative potential of cloud-native solutions. By reducing runtime, scaling dynamically, and enhancing efficiency, APT is better positioned to advance its mission of combating antimicrobial resistance.<\/span><\/p>\n<p>&nbsp;<\/p>\n[\/et_pb_text][et_pb_button button_url=&#8221;https:\/\/outlook.office365.com\/owa\/calendar\/PTP1@pinnacletechpartners.com\/bookings\/&#8221; url_new_window=&#8221;on&#8221; button_text=&#8221;Schedule a call&#8221; button_alignment=&#8221;left&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; custom_button=&#8221;on&#8221; button_text_size=&#8221;18px&#8221; button_text_color=&#8221;#ffffff&#8221; button_bg_color=&#8221;gcid-primary-color&#8221; button_border_width=&#8221;0px&#8221; button_border_radius=&#8221;50px&#8221; button_font=&#8221;Ubuntu|500|||||||&#8221; button_use_icon=&#8221;off&#8221; custom_padding=&#8221;0.8rem|1.8rem|0.8rem|1.8rem|true|true&#8221; button_text_size_tablet=&#8221;1rem&#8221; button_text_size_phone=&#8221;1rem&#8221; 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Engaging PTP&#8217;s CloudOps team, APT transitioned their pipeline from SnakeMake to Nextflow on an AWS Batch environment, reducing runtime from 5-6 hours to 1.15 hours using 650 CPUs. This case study showcases how PTP optimized APT\u2019s workflow for faster data processing and analysis.<\/p>\n","protected":false},"author":445,"featured_media":16795,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"<p>In the fascinating and high-stakes domain of biotechnology, research validation and acquisition of the next wave of funding stand as crucial checkpoints in the journey of a startup. Amid an environment characterized by escalating scrutiny and disturbing reports of manipulated outcomes, it becomes critical to build an infrastructure that is not only efficient but also transparent and reliable. Our client in the startup life sciences space needed to build out their environment for informatics on AWS while requiring PTP help significantly expedite the research process and, most importantly, validate its authenticity.<\/p><p>The primary goal in fortifying the validity of research is in streamlining and automating the data processing pipelines. The science required the expertise to support scaling homegrown pipelines, industry-leading solutions such as Nextflow, which provides a scalable and reproducible scientific workflow using software containers, as well as open-source conglomerates like Cell Ranger, Seurat, Picard, and Star Aligner, which have shown proven results in genomics and computational biology. This client demanded a cloud pipeline solution that was automated, repeatable, easily changeable and fully documents to ensure research validation. These solutions, when interlaced with robust AWS offerings like EC2, ELB, Auto Scaling, Lambda, and Fargate, create a scalable, cost-efficient, and high-throughput data processing solution that stands all the major test of validation.<\/p><p>PTP leveraged EC2 Image Builder and Service Catalogs to produce images in a controlled and repeatable manner. This allows for scientists and informaticians to independently launch pipelines through Service Catalog. These users have limited permissions to just launch Service Catalog everything else is controlled through the code process and permissions are minimized by the security group for control.<\/p><p>PTP centralized the building of images in one account and that account shares across the organization into those required accounts which exchange information between accounts with Amazon Parameter Store.<\/p><p>Image building was automated using EC2 Image Builder allowing PTP to build different standard images for different functions. From there the team created a recipe in Image Builder containing the software components that make up the image and defines the ownership of the component. This provides complete documentation on what software and versions are installed, which in life sciences is essential for controlling variables and seeking research validation. This Build account has access to private and controlled code repositories so that software version can be frozen or recreated from any point in time<\/p><p>These builds were all written into Terraform to maintain the image files and component lists and version controlled by AWS Code Commit. As components change in Terraform, for example a software update to \u201cversion 4.2\u201d, Terraform will know the file has changed and will deploy a new version of the component which then creates a new version of the recipe in Image Builder.<\/p><p>For cost optimization, the Service Catalog services are tied to Cloudwatch events that trigger when devices go idle, then SQS queue and Lambda are used to terminate resources they go idle for a period of time. When services\/images are recreated, they are automatically reconnected to persistent storage. \u00a0Going forward, PTP is working with this client to incorporate Amazon WorkSpaces and AWS Managed AD to further isolate data and create additional levels of control and security.<\/p><p>The result of this design and infrastructure-as-code implementation is a data management platform that will aid in the effort of research validation due to the limitation of variables and changes. The team also estimates between a 50-75% savings reduction driven through the automated deployment and tear-down of resources for use only when called upon verses building a traditional cloud computing environment. Lastly, the least-privilege access configurations enhance the protection of sensitive data which aligns with the consistent approach to the build of a Well Architected AWS environment.<\/p><p>\u00a0<\/p><h3>Purchase PTP's <a href=\"https:\/\/aws.amazon.com\/marketplace\/pp\/prodview-it7fjq6rqix74?sr=0-13&ref_=beagle&applicationId=AWSMPContessa\">CloudOps Offer<\/a> on AWS Marketplace!<\/h3><p>\u00a0<\/p><h3>Learn More about PTP's CloudOps <a href=\"https:\/\/ptp.cloud\/cloud-ops\/\">HERE<\/a><\/h3>","_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":[23,12,14],"tags":[76],"table_tags":[],"class_list":["post-14431","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-aws-archive","category-aws-for-life-sciences-archive","category-case-studies-archive","tag-aws"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.1.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How APT Cut Pipeline Runtimes by 80% with AWS Batch and Nextflow<\/title>\n<meta name=\"description\" content=\"Explore how Adaptive Phage Therapeutics revolutionized antimicrobial research, slashing processing time by over 75% with AWS infrastructure.\" \/>\n<meta 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