Icahn School of Medicine at Mount Sinai
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Resources Genomics Core Facility Bioinformatics


The Genomics Core Facility has ample computing power and software for data storage and analysis. Primary storage and computing capacity is built to gather real time data coming from second and third generation sequencers, to carry out primary analysis and to distribute data to users. It has the following features:

  • Primary Storage: 100 TB high-performance parallel file system with N/2 redundancy
  • RAM: 96 GB per node
  • Processing: 192 cores at 3.46 GHz each
  • Average demultiplexing (or primary) pipeline run-time for a 100bp paired-end run : 2-3 hours
  • Each sequencing machine is directly connected to the Server via a 1 Gigabit network mount

In addition, high performance computing (HPC) infrastructure called Minerva has been set up. Its features include the following:

  • 7,680 Advanced Micro Devices (AMD) 2.3 GHz Interlagos cores
  • 120 Dell C6145, 2 blade chassis nodes in six cabinets
  • 64 compute cores in four sockets and 256 Gigabytes (GB)s of memory per node
  • 30 Terabytes (TB) of RAM
  • Interconnected with Mellanox Quad Data Rate (QDR 40 Gbps) Fat-Tree Infiniband
  • 1.5 Petabytes (PB) of Data Direct Networks (DDN) SFA10K high-speed storage (600 disk drives in two cabinets), 10 GB/s
  • Dual 10 Gigabit Ethernet links to the Sinai campus network

Bioinformatics Support

Currently Available

  • Analysis of basic sequence quality and run parameter QC using Illumina CASAVA software, and SMART software of Pac Bio
  • Alignment of genome/exome/chromatome sequence with reference genome using CASAVA and BWA pipelines
  • Secondary analysis of genome/exome sequences using GATK pipeline, output will include a list of SNPS, SNVs, and indels
  • Alignment of RNA-Seq data by TopHat analysis pipeline
  • Analysis of transcriptome expression profile using Cuff Link software
  • Model-based Analsysis of ChIP-Seq data (MACS)

Research Project Driven Tertiary Analysis

  • Collaboration with Institute for Genomics and Multiscale Biology Faculty