Evidence map›Paper›PMID 27789525›Full record

ArticleGenome research2016

A privacy-preserving solution for compressed storage and selective retrieval of genomic data.

Zhicong Huang, Erman Ayday, Huang Lin, Raeka S Aiyar, Adam Molyneaux, Zhenyu Xu, Jacques Fellay, Lars M Steinmetz, Jean-Pierre Hubaux

Abstract read
In one paragraph

Article in Genome research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Review
  6. Article
  7. Predicting the Occurrence of Variants in RAG1 and RAG2.Journal of clinical immunology · 2019
    Article
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Zhicong HuangSchool of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
Erman AydayDepartment of Computer Engineering, Bilkent University, Bilkent 06800 Ankara, Turkey.
Huang LinSchool of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
Raeka S AiyarStanford Genome Technology Center, Stanford University, Palo Alto, California 94304, USA.
Adam MolyneauxSophia Genetics, CH-1025 Saint-Sulpice, Switzerland.
Zhenyu XuSophia Genetics, CH-1025 Saint-Sulpice, Switzerland.
Jacques FellaySchool of Life Sciences, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.ORCID 0000-0002-8240-939X
Lars M SteinmetzStanford Genome Technology Center, Stanford University, Palo Alto, California 94304, USA.ORCID 0000-0002-3962-2865
Jean-Pierre HubauxSchool of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In clinical genomics, the continuous evolution of bioinformatic algorithms and sequencing platforms makes it beneficial to store patients' complete aligned genomic data in addition to variant calls relative to a reference sequence. Due to the large size of human genome sequence data files (varying from 30 GB to 200 GB depending on coverage), two major challenges facing genomics laboratories are the costs of storage and the efficiency of the initial data processing. In addition, privacy of genomic data is becoming an increasingly serious concern, yet no standard data storage solutions exist that enable compression, encryption, and selective retrieval. Here we present a privacy-preserving solution named SECRAM (Selective retrieval on Encrypted and Compressed Reference-oriented Alignment Map) for the secure storage of compressed aligned genomic data. Our solution enables selective retrieval of encrypted data and improves the efficiency of downstream analysis (e.g., variant calling). Compared with BAM, the de facto standard for storing aligned genomic data, SECRAM uses 18% less storage. Compared with CRAM, one of the most compressed nonencrypted formats (using 34% less storage than BAM), SECRAM maintains efficient compression and downstream data processing, while allowing for unprecedented levels of security in genomic data storage. Compared with previous work, the distinguishing features of SECRAM are that (1) it is position-based instead of read-based, and (2) it allows random querying of a subregion from a BAM-like file in an encrypted form. Our method thus offers a space-saving, privacy-preserving, and effective solution for the storage of clinical genomic data.

Indexed as

AlgorithmsComputational BiologyComputer SecurityData CompressionGenetic PrivacyGenome, HumanGenomicsHumansInformation Storage and Retrieval

Identifiers

PMID27789525
PMCPMC5131820

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.