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John Rachlin

Assistant Teaching Professor

Rachlin, John

Contact

WPersonal site

GGoogle Scholar

Ej.rachlin@northeastern.edu

Office Location

105-107 Forsyth Street
132A Nightingale Hall
Boston, MA 02115

Mailing Address

Northeastern University
ATTN: John Rachlin, 202 WVH
360 Huntington Avenue
Boston, MA 02115-5000

Research Interests

  • Databases
  • Artificial intelligence
  • Bioinformatics
  • Large-scale computing

Education

  • PhD in Computer Science, Boston University
  • MS in Computer Science, Johns Hopkins University
  • BA in English and Physics, Cornell University

Biography

John Rachlin is an assistant teaching professor in the Khoury College of Computer Sciences at Northeastern University. Prior to joining Northeastern, he was a Staff Software Engineer at Fitbit and a Principal Software Engineer at Optum Analytics. At Optum, he developed tools and technologies to integrate and analyze patient electronic medical record (EMR) data as part of the company’s transition to Hadoop-based distributed computing. He is also the Co-Founder of Diatom, LLC, a software consultancy specializing in bioinformatics, healthcare, and big-data solutions.

About me:

Where did you grow up?

I grew up in Vermont on the shores of Lake Champlain

What are the specifics of your educational background?

My PhD thesis presented a novel graph-theoretic model called multi-node graphs where edges are activated or deactivated as a function of the state of their incident vertices. Such graphs are useful for modeling biological networks and for optimizing constraints in multiplex PCR assay design.

What is your research focus in a bit more detail?

My current research is focused on the development of scalable multi-objective optimization and decision-support systems using evolutionary algorithms.

What courses/subjects do you teach?

  • CS 1800: Discrete Structures
  • CS 3200: Database Design
  • CS 5002: Discrete and Data Structures
  • CS 5200: Database Management Systems
  • DS 2000: Programming with Data
  • DS 4300: Large-Scale Storage and Retrieval

Previous Publications

2015

COMBREX-DB: an experiment centered database of protein function: knowledge, predictions and knowledge gaps

Chang YC, Hu Z, Rachlin J, Anton BP, Kasif S, Roberts RJ, Steffen M (2016). COMBREX-DB: an experiment centered database of protein function: knowledge, predictions and knowledge gaps. Nucleic Acids Res. 44(DB):D330-5.

Abstract

The COMBREX database (COMBREX-DB; combrex.bu.edu) is an online repository of information related to (i) experimentally determined protein function, (ii) predicted protein function, (iii) relationships among proteins of unknown function and various types of experimental data, including molecular function, protein structure, and associated phenotypes. The database was created as part of the novel COMBREX (COMputational BRidges to EXperiments) effort aimed at accelerating the rate of gene function validation. It currently holds information on ∼3.3 million known and predicted proteins from over 1000 completely sequenced bacterial and archaeal genomes. The database also contains a prototype recommendation system for helping users identify those proteins whose experimental determination of function would be most informative for predicting function for other proteins within protein families. The emphasis on documenting experimental evidence for function predictions, and the prioritization of uncharacterized proteins for experimental testing distinguish COMBREX from other publicly available microbial genomics resources. This article describes updates to COMBREX-DB since an initial description in the 2011 NAR Database Issue.

2010

COMBREX: a project to accelerate the functional annotation of prokaryotic genomes

Roberts R.J., Chang Y-C, Hu Z., Rachlin J., et al.; 2010. COMBREX: a project to accelerate the functional annotation of prokaryotic genomes. Nucleic Acid Res. 39, D11-D14.

Abstract

COMBREX ( http://combrex.bu.edu ) is a project to increase the speed of the functional annotation of new bacterial and archaeal genomes. It consists of a database of functional predictions produced by computational biologists and a mechanism for experimental biochemists to bid for the validation of those predictions. Small grants are available to support successful bids.

2007

Multi-Node Graphs: A Framework for Multiplexed Biological Assays

Alon N., Asodi V., Cantor C., Kasif S., and Rachlin J.; 2006. Multi-node graphs: A framework for multiplexed biological assays. Journal of Computational Biology 13(10):1659-1672.

Abstract

Multiplex polymerase chain reaction (PCR) is an extension of the standard PCR protocol in which primers for multiple DNA loci are pooled together within a single reaction tube, enabling simultaneous sequence amplification, thus reducing costs and saving time. Potential cost saving and throughput improvements directly depend on the level of multiplexing achieved. Designing reliable and highly multiplexed assays is challenging because primers that are pooled together in a single reaction tube may cross-hybridize, though this can be addressed either by modifying the choice of primers for one or more amplicons, or by altering the way in which DNA loci are partitioned into separate reaction tubes. In this paper, we introduce a new graph formalism called a multi-node graph, and describe its application to the analysis of multiplex PCR scalability. We show, using random multi-node graphs that the scalability of multiplex PCR is constrained by a phase transition, suggesting fundamental limits on efforts to improve the cost-effectiveness and throughput of standard multiplex PCR assays. In particular, we show that when the multiplexing level of the reaction tubes is roughly Θ(log (sn)) (where s is the number of primer pair candidates per locus and n is the number of loci to be amplified), then with very high probability we can ‘cover’ all loci with a valid assignment to one of the tubes in the assay. However, when the multiplexing level of the tube exceeds these bounds, there is no possible cover and moreover the size of the cover drops dramatically. Simulations using a simple greedy algorithm on real DNA data also confirm the presence of this phase transition. Our theoretical results suggest, however, that the resulting phase transition is a fundamental characteristic of the problem, implying intrinsic limits on the development of future assay design algorithms.

2005

Computational tradeoffs in multiplex PCR assay design for SNP genotyping

Rachlin J., Ding C., Cantor C., and Kasif S.; 2005. Computational tradeoffs in multiplex PCR assay design for SNP genotyping. BMC Genomics 6(1):102

Abstract

Multiplex PCR is a key technology for detecting infectious microorganisms, whole-genome sequencing, forensic analysis, and for enabling flexible yet low-cost genotyping. However, the design of a multiplex PCR assays requires the consideration of multiple competing objectives and physical constraints, and extensive computational analysis must be performed in order to identify the possible formation of primer-dimers that can negatively impact product yield.

MuPlex: multi-objective multiplex PCR assay design.

Rachlin J., Ding C., Cantor C., and Kasif S.; 2005. MuPlex: multi-objective multiplex PCR assay design. Nucleic Acids Res. 33(Web Server Issue):W544-7.

Abstract

We have developed a web-enabled system called MuPlex that aids researchers in the design of multiplex PCR assays. Multiplex PCR is a key technology for an endless list of applications, including detecting infectious microorganisms, whole-genome sequencing and closure, forensic analysis and for enabling flexible yet low-cost genotyping. However, the design of a multiplex PCR assays is computationally challenging because it involves tradeoffs among competing objectives, and extensive computational analysis is required in order to screen out primer-pair cross interactions. With MuPlex, users specify a set of DNA sequences along with primer selection criteria, interaction parameters and the target multiplexing level. MuPlex designs a set of multiplex PCR assays designed to cover as many of the input sequences as possible. MuPlex provides multiple solution alternatives that reveal tradeoffs among competing objectives. MuPlex is uniquely designed for large-scale multiplex PCR assay design in an automated high-throughput environment, where high coverage of potentially thousands of single nucleotide polymorphisms is required. The server is available at http://genomics14.bu.edu:8080/MuPlex/MuPlex.html.