Genomic and Personalized Medicine: V1-2

Genomic and Personalized Medicine : V1-2
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If the sample did not meet coverage, it was topped off for additional coverage. For the purpose of fingerprinting we extract a small aliquot from each sample prior to any of the processing for sequencing. This aliquot is genotyped on a set of 96 common SNPs. These SNPs have been carefully selected so that they are enable the identity validation of each of our read groups separately. This ensures that the aggregated sample comprising of about 24 reads groups consist of data only from the intended sample.

The methods given below were the same for Phases 1 and 2, except where noted otherwise. For Phase 1, all samples were sequenced at Macrogen with methods described in this section ; for Phase 2, some samples were sequenced at Macrogen and others at NWGC. Samples were assigned unique barcode tracking numbers and had a detailed sample manifest i. Initial QC entailed DNA quantification, sex typing, and molecular "fingerprinting" using a high frequency, cosmopolitan genotyping assay.

This 'fingerprint' was used to identify potential sample handling errors and provided a unique genetic ID for each sample, which eliminated the possibility of sample assignment errors. Samples were failed if: 1 the total amount, concentration, or integrity of DNA was too low; 2 the fingerprint assay produced poor genotype data or 3 sex-typing was inconsistent with the sample manifest. Barcoded plates were shipped to Macrogen for library construction and sequencing.

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Libraries were constructed with a minimum of 0. A second Bead cleanup was performed after ligation to remove any residual reagents and adapter dimers. Eight normalized and indexed libraries were pooled together and denatured before cluster generation on a cBot. For cluster generation, every step was controlled by cBot.

When cluster generation was complete, the clustered patterned flow cells were then sequenced with sequencing software HCS HiSeq Control Software. All aligned read data were subject to the following steps: 1 "duplicate removal" was performed, i. All QC metrics for both single-lane and merged data were reviewed by a sequence data analyst to identify data deviations from known or historical norms. The methods were the same for Phases 1 and 2, except where noted otherwise. Array genotypes were used to estimate sample contamination using VerifyIDintensity , for sample fingerprinting, and for downstream quality control of sequencing data.

Investigator was notified of samples that failed QC for total mass, degradation or contamination, and replacement samples were submitted. Briefly, genomic DNA was sheared using the Covaris LE sonicator to a target size of bp t; Duty; PIP; cycles , followed by end-repair and bead based size selection of fragmented molecules 0.

A Parallel Software Pipeline for Personalized Medicine[v1] | Preprints

The selected fragments were A-tailed, and sequence adaptors ligated onto the fragments, followed by two bead clean-ups of the libraries 0. Final libraries were multiplexed for 8 samples per sequencing lane, with each sample pool sequenced across 8 flow cell lanes.


The library pools were quantified by qPCR, loaded on the to HiSeq X patterned flow cells and clustered on an Illumina cBot following manufacturer's protocol. Demultiplexing of sequencing data was performed with bcl2fastq2 v2. Data was further processed using the GATK best-practices pipeline v3.

Individual sample BAM files were squeezed using Bamutil v1. Sample identity and sequencing data quality was confirmed by concordance to SNP array genotypes. Gender was determined from X- and Y-chromosome coverage and checked against submitter information. Further QC included review of alignment rates, duplicate rates, and insert size distribution. Metrics used for review of SNV and indel calls included: the total number of variants called, the ratio of novel to known variants, and the Transition to Transversion ratios, and the ratio of heterozygous to homozygous variant calls.

Methods were the same for both studies, except for those in the "Clustering and Sequencing" section below. Additional studies have provided small numbers of "legacy" samples. These were sequenced by Illumina to 30x depth prior to the start of the TOPMed project and have been remapped and included in the freeze 5b call set.

Project samples were processed from well barcoded plates provided by Illumina. This enabled a seamless interface with robotic processes and retained sample anonymity. An aliquot of each sample was processed in parallel through the Infinium Omni 2. Samples were batched using LIMS, and liquid handling robots performed library preparation to guarantee accuracy and enable scalability. All sample and reagent barcodes were verified and recorded in LIMS. Samples were fragmented and libraries are size selected following fragmentation and end-repair using paramagnetic sample purification beads, targeting short insert sizes.

Final libraries were quality controlled for size using a gel electrophoretic separation system and awee quantified. Clustered v4 flow cells were loaded onto HiSeq instruments and sequenced on bp paired-end, non-indexed runs. All samples were sequenced on independent lanes. Clustered patterned flow cells were loaded onto HiSeq X instruments and sequenced on bp paired-end, non-indexed runs. The Whole Genome Sequencing Service leverages a suite of proven algorithms to detect genomic variants comprehensively and accurately. Most versions of the Illumina callers are open source and available publicly.

One or more lanes of data were processed from run folders directly with the internal use only ISAS framework 2. The genome build QC pipeline was automated to evaluate both primary sequencing level and secondary build level metrics against expectations based on historical performance. Genome builds that were flagged as outliers at QC are reviewed by our scientists for investigation.

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Libraries or sequencing lanes were requeued for additional sequencing or library prep as needed. Samples had a detailed sample manifest i. Starting with minimum of 0.

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The resulting sheared DNA was selectively purified by sample purification beads to make the precise length of insert. KK without amplification KR v1. Briefly, validated libraries were denatured, diluted and clustered onto v2. Illumina sequencing instruments, including HiSeqX, generate per-cycle BCL base call files as primary sequencing output. Before aligning steps, the proportion of base quality Q30 was checked. During alignment steps, the duplicated reads were marked and not used for variant calling.

Moreover, we check the proportion of GC, insert size, and Depth of Coverage mode of sequence depth, interquartile range of depth and distance from Poisson distribution , when the proportion of 10X coverage failed. The metadata were linked at intake to a unique and de-identified sample identifier NWD ID , which was propagated through all phases of the pipeline.

This unique identifier was subsequently embedded in the library name, all sequencing events, and all deliverable files. Two independent methods were used to determine the quantity and quality of the DNA before library construction including 1 Picogreen assays and 2 E-Gels. This assay was setup in well plates using a Biomek robot and fluorescence determined using the Synergy 2 fluorescence spectrophotometer. Semi-quantitative and qualitative "yield gels" were used to estimate DNA sample integrity. These gels also served indirectly as a "cross-validation" for the Picogreen assay since the same standards were used in both assays.

This assay addresses specific attributes around gender, and polymorphisms across populations and ancestry. It also assists in early stage contamination detection, and is used to validate sample concordance against the final sequence files to ensure pipeline integrity. Libraries were routinely prepared using Beckman robotic workstations Biomek FX and FXp models in batches of 96 samples and all liquid handling steps were incorporated into the LIMS tracking system. A double size selection step was then employed, with different ratios of AMPure XP beads, to select a narrow band of sheared DNA for library preparation.

Genomic and Personalized Medicine: V1-2

DNA end-repair and 3'-adenylation were performed in the same reaction followed by ligation of the barcoded adaptors to create PCR-Free libraries. This protocol allowed for the routine preparation of well library plates in 7 hours. Both of these assays were done in batches of 96 samples in hours. Automated library construction and quantification procedures routinely included a positive and negative control no template control on every well library construction plate to monitor process consistency and possible contamination events.

Optimal library concentrations used for cluster generation were determined before releasing libraries into production. Typical loading concentrations range between pM. Run performance was monitored through key metrics using the current HiSeq X instrument software 3. One sample was loaded per HiSeq X lane to achieve a minimum coverage of 30X, or 90 Gbp of unique reads aligning to the human reference per sample.

Overall run performance was evaluated by metrics from the off-instrument software Casava and from mapping results generated by the Mercury HgV analysis pipelines. Duplicate, unmapped, and low quality reads were flagged rather than filtered. A series of QC metrics were calculated after the mapping step. Sample concordance was measured by comparing SNP Trace genotype calls for a given sample to alignment-based genotype calls from that sample. The concordance report includes both self-concordance and the top six next best concordant samples.

The IRC pipeline consists of two major processes diagrammed in the Figure 1 below: 1 Harmonization of data from the BAM files provided by the Sequencing Centers and 2 joint variant discovery and genotype calling across studies. Detailed protocols for these processes are given in the following sections. Ahead of joint variant discovery and genotype calling by the IRC, the sequence data obtained from the TOPMed Sequencing Centers were remapped using a standard protocol to produce "harmonized" sequence data files.

Sequence data were received from each sequencing center in the form of. File transfer was via Aspera or Globus Connect, depending on the center. Batches of - The IRC validated the md5 checksum, indexed each. In-house scripts were used to read group tags as needed to legacy Illumina sequencing data from To produce the harmonized read mappings which were used for variant discovery and genotyping, we remapped the sequence data in each.

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We used 'bamUtils bam2fastq' with flags '--splitRG --merge --gzip' to extract all sequence reads by read group into interleaved. Samblaster v. Read group header information was copied verbatim from the incoming sequencing center alignment file. Samtools version 1.

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ML approaches generally require the standard annotated training data sets for which the generation process is usually time- and labor-consuming Krallinger et al. Free delivery worldwide. Spara som favorit. Their first priorities are to identify and enrol patients suitable for the , Genomes Project and drive forward the national training agenda. Therefore, true causal variants for predominantly non-European patients are likely to fall into the NAV categories.

Processing was coordinated and managed by in-house scripts. DNA sample contamination was estimated from the sequencing center read mapping using an updated version of the software verifyBamId Goo Jun, et al. American Journal of Human Genetics, v. New procedures to access the individual level sequence data files mapped to build 38 are currently under technical development.

An implementation timeline is not currently available. The following description refers to specific components of the pipeline. The GotCloud pipeline detects variant sites and calls genotypes from a list of aligned sequence reads. Specifically, the pipeline for freeze 5b consisted of the following six key steps see also Figure 2.

Most of these procedures will be integrated into the next official release of GotCloud software package. The candidate variants were normalized by vt normalize algorithm. Estimation of contamination, genetic ancestry, and sex : For each sequenced genome, genetic ancestry and DNA sequence contamination were estimated by the cramore cram-verify-bam software tool. In addition, the biological sex of each sequenced genome was inferred by computing relative depth at X and Y chromosomes compared to the autosomal chromosomes, using the software tool cramore vcf-normalized-depth.

Genotype and feature collection : For each batch of 1, samples and 10Mb of chunks,the genotyping module implemented in cramore dense-genotype collects individual genotype likelihoods and variant features across the merged sites by iterating over sequenced genomes, focusing on the selected region, using the contamination levels and sex inferred in step 2. These per-batch genotypes are merged across all batches for each kb region, using the cramore paste-vcf-calls software tool, producing merged and unfiltered genotypes.

The estimated genetic ancestry of each individual was used as input when merging genotypes to compute variant features involving individual-specific allele frequencies. These genotypes and inferred sex at step 2 were used together to infer a pedigree consisting of duplicated individuals and nuclear families using king2 and vcf-infer-ped software tools.

Variant filtering : We use the inferred pedigree of related and duplicated samples to calculate Mendelian consistency statistics using vt milk-filter, and to train a variant classifier using a Support Vector Machine SVM implemented in the libsvm software package. A sequence index file. Modifying the first section index and ped file in particular should be minimally required changes. We encourage comments and feedback from a broad range of readers.

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Create alert Email:. Load new image. Preprint Article Version 1 This version is not peer-reviewed. Cite as: Agapito, G. Personalized medicine is an aspect of the P4 medicine predictive, preventive, personalized and participatory based precisely on the customization of all medical characters of each subject. In personalized medicine, the development of medical treatments and drugs is tailored to the individual characteristics and needs of each subject, according to the study of diseases at different scales from genotype to phenotype scale. For example, by using genotyping microarrays e. A side effect of high-throughput methodologies is the massive amount of data produced for each single experiment, that poses several challenges e.

Thus a main requirement of modern bioinformatic software is the use of good software engineering methods and efficient programming techniques, able to face those challenges, that include the use of parallel programming and efficient and compact data structures. Thus, to exploit all the potential of this massive amount of data in the short possible time before that data becomes obsolete , the necessity to develop parallel software tools for efficient data collection and analysis arise.

Moreover, due to the heterogeneity of the data produced by the different kinds of experimental platforms, it is necessary to automatize in a comprehensive software pipeline, the various steps that compose a bioinformatic analysis, such as: the preprocessing of raw data to remove noise or corrupted data; the annotation of data with external knowledge e.

Gene Ontology , and the integration of molecular data with clinical data. It should be noted that such steps are necessary to make statistical or data mining analysis more effective. This paper presents the design and the experimentation of a comprehensive software pipeline, named microPipe, for the preprocessing, annotation and analysis of microarray-based SNP genotyping data.

Personalized Medicine: A New Approach - Luigi Boccuto - TEDxGreenville

A case study in pharmacogenomics is presented. The main advantages of using microPipe are: the reduction of errors that may happen when trying to make data compatible among different tools; the possibility to analyze in parallel huge datasets; the easy annotation and integration of data. SNP; multiple analysis pipeline; pharmacogenomics; overall survival curves; data mining: statistical analysis.

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Your full name. Your affiliation information e. Your Email address not made public. Importance: How significant is the paper to the field? Significant contribution. Incremental contribution. No contribution. I am not qualified to judge. Conclusions well supported. Most conclusions supported minor revision needed. Incomplete evidence major revision needed.