The Genomics Sequencing Center (GSC) is a “one-stop” shop for the development and application of state-of-the-art Next-Generation Sequencing (NGS) technologies. Researchers are provided rapid and cost-effective high quality experimental design, data, and data analysis by genomics experts.

NGS Services


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Services | Deliverables | Turnover Times | Pricing | Sample Submission | Request a Quote

Overview

GSC scientists specialize in the development and application of cutting-edge Next-Generation Sequencing (NGS) technologies and bioinformatics analyses. The following NGS technologies and services are offered: 

 

NGS Platforms

  • NovaSeq 6000: This is Illumina’s groundbreaking platform, which enables scalable throughput and flexibility for any sequencing method, genome, and scale of project. GSC can leverage this instrument using 1, 2, 3, and 4 lane flow cells, to multiplex 96-384 samples or more per run, thus providing the best cost-to-benefit NGS services to VT researchers. NovaSeq can generate 8-10 billion sequencing reads (Single Reads) on a S4 flow cell and sequence 2 x 150 bp read lengths. Specifications are available at this link.
  • NextSeq 500: This system brings GSC the flexibility for a fast turnaround for smaller projects. With tunable output and high data quality, it provides flexibility for whole-genome, transcriptome, and targeted resequencing. NextSeq can generate up to 400 million sequencing reads (Single Reads) on a High Output flow cell and sequence 2 x 150 bp read lengths. Specifications of NextSeq are available at this link.
  • MiSeq: This system is focused on applications such as targeted sequencing, 16S metagenomics, small genome sequencing, targeted gene expression profiling and other amplicon sequencing. Miseq can generated up to 25 million sequencing reads (Single Reads) on a High Output flow cell and sequence 2 x 300 bp read lengths. Specifications of MiSeq are available at this link.

Highlights

  • The GSC is a 6,500 sq. ft. NGS center, housing Illumina’s HiSeq 2500, MiSeq, automation for library preparation, 2-D barcoding and sample tube tracking instruments, Covaris M220, robotics, and ABI’s ViiA7.
  • The GSC provides comprehensive genomic and epigenomic next-gen sequencing and bioinformatics services, delivering rapid, cost-effective, and high-quality results to meet our users' research needs.
  • In addition to established protocols, the GSC specializes in customizing NGS services to suit investigators’ needs.

Services

Transcriptomics

  • mRNA-Seq: Stranded and non-stranded, high levels of multiplexing
  • Stranded Total RNA-Seq:  polyA/non-polyA
  • Small RNA-Seq: High levels of multiplexing
  • Difficult samples: Low input, single cell, LCM, FFPE samples, both stranded and non-stranded
  • Strand-specific sequencing
  • Microbial rRNA depletion and RNA-Seq with amounts as low as 1 ug of total RNA

Genomics

  • Whole Genome Sequencing
    • Human / Animal / Plant
    • Microbial
  • De novo Sequencing
  • Exome/Targeted capture re-sequencing: Enables high sequencing depths
    • Agilent and Illumina platforms
    • Human, Mouse, Canine and other species
  • Targeted re-sequencing: High levels of multiplexing up to 200 samples / MiSeq run
    • PCR Amplicon sequencing
    • Illumina and Agilent platforms

Gene Regulation

  • ChIP-Seq
    • Transcription factor analysis
    • Histone modifications
  • DNA Methylation
    • MeDIP- and MBD-Seq
    • MethylC-Seq
    • Agilent SureSelect MethylC-Seq
    • Nucleosome Mapping
      • FAIRE-Seq and DNAse I-Seq

Metagenomics

  • 16S / 18S / ITS amplicon sequencing
  • Whole Genome Metagenomic sequencing
  • Metatranscriptomic analysis

Other Services

  • DNA/chromatin fragmentation by Covaris
  • DNA / RNA quality analysis:  BioAnalyzer / TapeStation assay, Qubit (Picogreen) assays
  • qPCR services

Deliverables


NovaSeq 6000 Specifications

Sequencing performance parameters
Sequencing Output Per Flow Cell
Flow Cell Type S1* S2 S4
2 x 50 bp 134-167 Gb 280-333 Gb N/A
2 x 100 bp 266-333 Gb 560-667 Gb N/A
2 x 150 bp 400-500 Gb 850-1000 Gb 2400-3000 Gb
Reads Passing Filter Per Flow Cell (Single Reads)
Flow Cell Type S1* S2 S4
  1.3-1.6 billion 2.8-3.3 billion 8-10 billion
Quality Scores† and Run Time‡
Flow Cell Type S1* S2 S4
Quality Scores
2 x 50 bp  ≥ 85%
2 x 100 bp ≥ 80%
2 x 150 bp ≥ 75%
Run Time
2 x 50 bp ~13 hours ~16 hours N/A
2 x 100 bp ~18 hours ~25 hours N/A
2 x 150 bp ~24 hours ~36 hours ~44 hours

Specifications based on Illumina PhiX control library at supported cluster densities. These numbers vary with customer samples

* NovaSeq S1 Reatgent Kits are not available.
† A quality score (Q-Score) is a prediction of the probability of an error in base calling. The percentage of bases > Q30 is averaged across the entire run. Quality scores are based on NovaSeq Reagent Kits run on the NovaSeq 6000 System using an Illumina PhiX control library. Performance may vary based on library type and quality, insert size, loading concentration, and other experimental factors.
‡ Run time includes cluster generation, sequencing, and base calling. Run times are based on running 2 flow cells of the same type; starting two different flow cells will impact run time


NextSeq 500 Specifications and Deliverables

Sequencing Performance Perameters
High-Output Kit* Mid-Output Kit*
Read Length Total Time† Output Read Length Total Time† Output
2x150 bp 29 hours 100-120 Gb 2 x 150 bp 26 hours 32.5-39 Gb
2 x 75 bp 18 hours 50-60 Gb 2 x 75 bp 15 hours 16.3-19.5 Gb
1 x 75 bp 11 hours 25-30 Gb      
Reads Passing Filter
High-Output Kit* Mid-Output Kit*
Up to 400 million single reads Up to 130 million single reads
Up to 800 million paired-end reads Up to 260 million paired-end reads
Quality Scores
High-Output Kit* Mid-Output Kit
> 75% bases higher than Q30 at 2 x 150 bp > 75% bases higher than Q30 at 2 x 150 bp
> 80% bases higher than Q30 at 2 x 75 bp > 80% bases higher than Q30 at 2 x 75 bp
> 80% bases higher than Q30 at 1 x 75 bp  


*Install specifications based on Illumina PhiX control library at supported cluster densities (between 129 and 165 k/mm² clusters passing filter). Actual performance parameters may vary based on sample type, sample quality, and clusters passing filter. All NextSeq 500 kits are paired-end capable.
†Total time includes cluster generation, sequencing, and base calling on a NextSeq 550 System enabled with dual-surface scanning.
‡ A quality score (Q-score) is a prediction of the probability of an error in base calling. The percentage of bases > Q30 is averaged across the entire run.


MiSeq Specifications and Deliverables

sequencing performance perameters
Read Length †Reads Passing Filter Output Quality Scores (Q-Score)(% bases above Q30) *Run Time
MiSeq Reagent Kit V2
1 x 50 12-15 million single reads or 24-30 million paired end reads 750-850 Mb > 90% 5 hrs
2 x 25 750-850 Mb > 90% 5.5 hrs
2 x 150 4.5-5.1 Gb > 80% 24 hrs
2 x 250 7.5-8.5 Gb > 75% 39 hrs
MiSeq Reagent Kit V3
1 x 150 22-25 million
single reads or
44-50 million
paired end reads
3.3-3.8 Gb > 80% 19 hrs
2 x 75 3.3-3.8 Gb > 85% 20 hrs
2 x 300 13.2-15 Gb > 70% 55 hrs

*Total time includes cluster generation, sequencing, and base calling on a MiSeq system.
†Performance may vary based on samples quality, cluster density, applications, and other experimental factors. Low diversity libraries are run at a lower cluster density.
 

Turnover Times

RNA and DNA QC

  • Timeframe: two days to one week
  • Includes: NanoDrop, Qubit, BioAnalyzer, or TapeStation
  • Details: Upon delivery, GSC staff provide information on whether the samples can move forward for library preps and sequencing

Library Preparation

  • Timeframe: one to three weeks
  • Includes: library preps, QC and quantitation
  • Details: the delivery timeframe resets if library preps fail and the user submits replacement samples

Sequencing

  • NextSeq: Sequencing times of 1-2 days depending on the length of sequencing. There is no lane sharing in NextSeq.
  • NovaSeq: Sequencing times of 1-2 days depending on the length of sequencing. 
  • MiSeq: 1-3 days depending on the length of sequencing. 

Pricing

For up-to-date information on the pricing structure of GSC services, as well as official refund and delivery policies, please reference our downloadable pricing guide.
 

Sample Submission

To submit a sample, please complete the appropriate forms and send them to our NGS representative at bi-gscinfo-g@vt.edu.

 

Sanger Services

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Services | Sample Requirements | Sample Submission | Turnover TimesDeliverables

Overview

In the last 50 years since Watson first discovered the structure of DNA, many advances have been made to enable researchers to study and dissect this macromolecule. Of primary interest for gene discovery efforts is the ability to precisely unravel the sequence of nucleotides comprising genes of interest. This unraveling process, called DNA sequencingis the main focus of the Genomics Sequencing Center's DNA sequencing group. The GSC currently offers two types of Sanger Sequencing Services

 

Services

Sanger Sequencing

DNA templates are isolated from a variety of sources, including plasmids and PCR reactions. These templates are then used in conjunction with template-specific primers, fluorescently labeled nucleotides and DNA polymerase to generate labeled fragments of complementary DNA. These fragments are then analyzed on an automated 48-capillary electrophoresis ABI 3730 DNA Analyzer and detected by a laser to generate a string of nucleotides representing the DNA sequence of the starting template.

 

The GSC offers two types of Sanger sequencing services

  • With full service DNA sequencing, GSC staff set up cycle sequencing reactions, perform clean-up, run the samples on the ABI 3730, and deliver the sequence data electronically.
  • The clean and run service is available for those who wish to perform the cycle sequencing reactions themselves. GSC staff simply perform clean-up, run the samples on the ABI 3730, and deliver the sequence data electronically.

Sample Requirements

In order to ensure the best quality results, samples submitted for analysis must meet certain requirements. Detailed guidelines for sample preparation can be found in our downloadable Sample Submission Requirements Guide

 

Sample Submission

Creating and Managing New Projects

All Sanger sequencing requests must go through our be processed through the GSC's Laboratory Information Management System (LIMS). If this is your first time using LIMS, please follow the instructions in our LIMS User Guide to create a new account and verify your payment method.

 

If you have any questions about your LIMS account or the sample submission process, please email us.
 

Sample Drop-Off

Customers who wish to drop off their samples in person can do so at the institute’s main reception desk between the hours of 8:00 am and 5:00 pm. In most cases, samples received by 8:30 am will be processed that same day.

If sending samples via mail, pack the samples with ice packs and include an information sheet with the researcher’s name and contact information and indicate that the samples are intended for Sanger sequencing. Send the package to:

Genomics Sequencing Center
Biocomplexity Institute of Virginia Tech
1015 Life Science Circle 
Blacksburg, VA 24061
Phone: (540) 231-1229

Turnover Times

Monday through Friday, our turnaround time is 48 hours, but we make every effort to complete the work within 24 hours. Our lab is closed on weekends so any Sanger samples submitted after 8:30 am on Friday will not be processed until Monday with results going out on Tuesday.

 

Deliverables

Upon completion of the sequencing run, you will be notified via email through the LIMS that your results are available for you to download. Log into your LIMS account and save these files (.ab1 files) to your computer. There are several software programs available online for viewing this type of sequencing file. For Sanger sequencing, we use Sequence Scanner 2 which is free downloadable software available from ABI.

Quality Control Services

Overview

Performing accurate quality control is the first step in obtaining quality sequencing results. The GSC offers a wide variety of quality control services to suit the needs of our clients.

 

Services

RNA and DNA Quantification

Note: we require a minimum of 5uL of sample submitted in 0.5mL tubes.

  • NanoDrop Spectrophotometer
  • Qubit Fluorometer (maximum concentration of 100 ng/ul based on NanoDrop readings

qPCR

Note: for library quality control, please submit 5-10uL of sample at a 2nM concentration.

 

​DNA/RNA Quality

  • Agilent Bioanalyzer
    • ​​High Sensitivity DNA Only - quantitative range of 0.005-0.5 ng/uL (0.1-0.5 ng/uL for fragmented DNA or DNA libraries); Sizing range of 50-7000 bp.
  • Agilent TapeStation
    • ​​RNA - quantitative range of 25-500 ng/uL.
    • High Sensitivity RNA - quantitative range of 0.5-10 ng/uL.
    • DNA1000 - quantitative range of 0.1-50 ng/uL; Sizing range of 35-1000 bp.
    • High Sensitivity DNA1000 - quantitative range of 0.01-1 ng/uL; Sizing range of 35-1000 bp.

DNA Shearing

  • Covaris M220
    • Please use this sample submission form when requesting Covaris services. Full submission requirements are listed on the form. 

Publications

GSC-Authored Research


1.  Monson M.S., Settlage R.E., McMahon K.W., Mendoza K.M., Rawal S., El-Nezami H.S., Coulombe R.A., Reed K.M., Response of the hepatic transcriptome to aflatoxin B1 in domestic turkey (Meleagris gallopavo). PLoS One. 2014 Jun 30;9(6).

2.  Dalloul R.A., Zimin A.V., Settlage R.E., Kim S., Reed K.M., Next-generation sequencing strategies for characterizing the turkey genome. Poult Sci. 2014 Feb;93(2):479-84.

3.  Monson M.S., Settlage R.E., Mendoza K.M., Rawal S., El-Nezami H.S., Coulombe R.A., Reed K.M., Modulation of the spleen transcriptome in domestic turkey (Meleagris gallopavo) in response to aflatoxin B1 and probiotics. Immunogenetics. 2015 Mar;67(3):163-78.

4.  Ding J, Reynolds LM, Zeller T, et al. Alterations of a Cellular Cholesterol Metabolism Network is a Molecular Feature of Obesity-Related Type 2 Diabetes and Cardiovascular Disease. Diabetes. 2015; 64: 3464-74.

5.  Liao X, Li S, Settlage RE, Sun S, Ren J, Reihl AM, Zhang H, Karyala SV, Reilly CM, Ahmed SA, Luo XM. (2015) Cutting Edge: Plasmacytoid Dendritic Cells in Late-Stage Lupus Mice Defective in Producing IFN-α. Journal of Immunology. 195:4578-4582.

6.  Zhang H, Sparks JB, Karyala SV, Settlage R, Luo XM. (2014) Host adaptive immunity alters gut microbiota. ISME Journal 9(3):770-81.

GSC-Impacted Research


7.   Metch JW, Burrows ND, Murphy CJ,  Pruden A, Vikesland PJ. Metagenomic analysis of microbial communities yields insight into impacts of nanoparticle design. Nature. 2018

8.  Reynolds LM, Ding J, Taylor JR, et al. Transcriptomic profiles of aging in purified human immune cells. BMC Genomics. 2015;16:333.  

9.  Yi H, Breheny P, Imam N, Liu Y, Hoeschele I. Penalized multimarker vs. single-marker regression methods for genome-wide association studies of quantitative traits. Genetics. 2015; 199:205–222.

10.  Liu Y, Ding J, Reynolds LM, et al. Methylomics of gene expression in human monocytes. Human Molecular Genetics. 2013:ddt356. 

11.  Zhang H, Liao X, Sparks JB, Luo XM. (2014) Dynamics of gut microbiota in autoimmune lupus. Applied Environmental Microbiology 80(24):7551-60.

12.  Mu Q, Zhang H, Luo XM (2015) SLE: Another Autoimmune Disorder Influenced by Microbes and Diet? Frontiers in Immunology doi: 10.3389/fimmu.2015.00608

13.  Zhang H, Luo XM (2015) Control of commensal microbiota by the adaptive immune system. Gut Microbes 6(2):156-60.

14.  Tae, H., Shallom, S., Settlage, R.E., Preston, D., Adams, L.G., Garner, H.R, Revised Genome Sequence of Brucella suis 1330. J. Bacteriology, 193(22), 2011, 6410.

15.  Tae, H., Shallom, S., Settlage, R.E., Hawkins, G.N., Adams, L.G., Garner, H.R, Complete Genome Sequence of Brucella suis VBI22, Isolated from Bovine Milk. J. Bacteriology, 194(4), 2012, 910.

16.  Tae H, Settlage RE, Shallom S, Bavarva JH, Preston D, Hawkins GN, Adams LG, Garner HR. Genomics.  Improved variation calling via an iterative backbone remapping and local assembly method for bacterial genomes. 2012 Nov;100(5):271-6.

17.  Radakovits, R., Jinkerson, R.E., Fuerstenberg, S.I., Tae, H., Settlage, R.E., Boore, J.L., Posewitz, M.C, Draft genome sequence and genetic transformation of the oleaginous alga Nannochloropsis gaditana, Nature Communications, 3:686, 2012.

18.  Bavarva J.H., Tae H., Settlage R.E., Garner H.R., Characterizing the Genetic Basis for Nicotine Induced Cancer Development: A Transcriptome Sequencing Study. PLoS One. 2013 Jun 18;8(6):e67252.

19.  Tae H., McMahon K.W., Settlage R.E., Bavarva J.H., Garner H.R., ReviSTER: an automated pipeline to revise misaligned reads to simple tandem repeats. Bioinformatics. 2013 Jul 15;29(14):1734-41.

20.  Zhang H., Wang H., Shepherd M., Wen K., Li G., Yang X., Kocher J., Giri-Rachman E., Dickerman A., Settlage R., Yuan L., Probiotics and virulent human rotavirus modulate the transplanted human gut microbiota in gnotobiotic pigs. Gut Pathog. 2014 Sep 9;6:39.

21.  Jiang X., Peery A., Hall A.B., Sharma A., Chen X.G., Waterhouse R.M., Komissarov A., Riehle M.M., Shouche Y., Sharakhova M.V., Lawson D., Pakpour N., Arensburger P., Davidson V.L., Eiglmeier K., Emrich S., George P., Kennedy R.C., Mane S.P., Maslen G., Oringanje C., Qi Y., Settlage R., Tojo M., Tubio J.M., Unger M.F., Wang B., Vernick K.D., Ribeiro J.M., James A.A., Michel K., Riehle M.A., Luckhart S., Sharakhov I.V., Tu Z.., Genome analysis of a major urban malaria vector mosquito, Anopheles stephensi. Genome Biol. 2014 Sep 23;15(9):459.

22.  Zhang H., Sparks J.B., Karyala S.V., Settlage R., Luo X.M., Host adaptive immunity alters gut microbiota. ISME J. 2015 Mar;9(3):770-81.

23.  Monson M.S., Settlage R.E., McMahon K.W., Mendoza K.M., Rawal S., El-Nezami H.S., Coulombe R.A., Reed K.M., Response of the hepatic transcriptome to aflatoxin B1 in domestic turkey (Meleagris gallopavo). PLoS One. 2014 Jun 30;9(6).

24.  Dalloul R.A., Zimin A.V., Settlage R.E., Kim S., Reed K.M., Next-generation sequencing strategies for characterizing the turkey genome. Poult Sci. 2014 Feb;93(2):479-84.

25.  Tae H., Kim D.Y., McCormick J., Settlage R.E., Garner H.R., Discretized Gaussian mixture for genotyping of microsatellite loci containing homopolymer runs. Bioinformatics. 2014 Mar 1;30(5):652-9.

26.  Monson M.S., Settlage R.E., Mendoza K.M., Rawal S., El-Nezami H.S., Coulombe R.A., Reed K.M., Modulation of the spleen transcriptome in domestic turkey (Meleagris gallopavo) in response to aflatoxin B1 and probiotics. Immunogenetics. 2015 Mar;67(3):163-78.

27.  Zhang W., Kim S., Settlage R., McMahon W., Sumners L.H., Siegel P.B., Dorshorst B.J., Cline M.A., Gilbert E.R., Hypothalamic differences in expression of genes involved in monoamine synthesis and signaling pathways after insulin injection in chickens from lines selected for high and low body weight. Neurogenetics. 2015 Apr;16(2):133-44.

28.  Zalenskaya I.A., Joseph T., Bavarva J., Yousefieh N., Jackson S.S., Fashemi T., Yamamoto H.S., Settlage R., Fichorova R.N., Doncel G.F., Gene Expression Profiling of Human Vaginal Cells In Vitro Discriminates Compounds with Pro-Inflammatory and Mucosa-Altering Properties: Novel Biomarkers for Preclinical Testing of HIV Microbicide Candidates. PLoS One. 2015 Jun 8;10(6).

29.  Ding J, Reynolds LM, Zeller T, Müller C, Lohman K, Nicklas BJ, Kritchevsky SB, Huang Z, de la Fuente A, Soranzo N, Settlage RE, Chuang CC, Howard T, Xu N, Goodarzi MO, Chen YD, Rotter JI, Siscovick DS, Parks JS, Murphy S, Jacobs DR Jr, Post W, et al. Alterations of a Cellular Cholesterol Metabolism Network Are a Molecular Feature of Obesity-Related Type 2 Diabetes and Cardiovascular Disease. Diabetes. 2015 Oct;64(10):3464-74.

30.  Hendrickson RC, Lee AY, Song Q, Liaw A, Wiener M, Paweletz CP, Seeburger JL, Li J, Meng F, Deyanova EG, Mazur MT, Settlage RE, Zhao X, Southwick K, Du Y, Holder  D, Sachs JR, Laterza OF, Dallob A, Chappell DL, Snyder K, Modur V, et al. High Resolution Discovery Proteomics Reveals Candidate Disease Progression Markers of  Alzheimer's Disease in Human Cerebrospinal Fluid. PLoS One. 2015;10(8):e0135365.

31.  Davis M, Jessee R, Close M, Fu X, Settlage R, Wang G, Cline MA, Gilbert ER. Fasting for 21days leads to changes in adipose tissue and liver physiology in juvenile checkered garter snakes (Thamnophis marcianus). Comp Biochem Physiol A Mol Integr Physiol. 2015 Sep 7;190:68-74. doi: 10.1016/j.cbpa.2015.09.001. [Epub ahead of print] PubMed [citation] PMID: 26358832

32.  Liao X, Li S, Settlage RE, Sun S, Ren J, Reihl AM, Zhang H, Karyala SV, Reilly CM, Ahmed SA, Luo XM. Cutting Edge: Plasmacytoid Dendritic Cells in Late-Stage Lupus Mice Defective in Producing IFN-α.  J Immunol. 2015 Nov 15;195(10):4578-82. doi: 10.4049/jimmunol.1501157. Epub 2015 Oct 7.

33.  Wang Z, Leary DH, Liu J, Settlage RE, Fears KP, North SH, Mostaghim A, Essock-Burns T, Haynes SE, Wahl KJ, Spillmann CM.  Molt-dependent transcriptomic analysis of cement proteins in the barnacle Amphibalanus amphitrite.  BMC Genomics. 2015 Oct 24;16(1):859. doi: 10.1186/s12864-015-2076-1.

34.  Zhan S, Merlin C, Boore JL, Reppert SM. The monarch butterfly genome yields insights into long-distance migration. Cell. 2011;147(5):1171–85.

35.  Kang L., Settlage R., McMahon W., Michalak K., Tae H., Garner H.R., Stacy E., Price D. & Michalak P. 2015. Genomic signatures of speciation in sympatric and allopatric Hawaiian picture-winged Drosophila. Genome Biology, under revision.

36.  Michalak K., Maciak S., Kim Y.B., Santopietro G., Oh J.H., Kang L., Garner H.R., & Michalak P. 20015. Nucleolar dominance and maternal control of 45S rDNA expression Proceedings of the Royal Society B: Biological Sciences, in press.

37.  Philipson C.W., Bassaganya-Riera J., Viladomiu M., Kronsteiner B., Abedi V., Hoops S., Michalak P., Kang L., Girardin S.E. & Hontecillas R. 2015. Modeling the regulatory mechanisms by which NLRX1 modulates innate immune responses to Helicobacter pylori infection. PLoS One 10(9):e0137839.

38.  Mechkarska M., Coquet L., Leprince J., Jouenne T., Vaudry H., Michalak K., Michalak P. &Conlon JM. 2014. Host-defense peptides from skin secretions of the octoploid frogs Xenopus vestitus and Xenopus wittei (Pipidae): Insights into evolutionary relationships. Comparative Biochemistry and Physiology – Part D Genomics Proteomics 11C: 20-28.

39.  Kim Y.B., Oh J.H., McIver L.J., Rashkovetsky E., Michalak K., Garner H.R., Kang L., Nevo E., Korol A.B. & Michalak P. 2014. Divergence of Drosophila melanogaster repeatomes in response to a sharp microclimate contrast in ‘Evolution Canyon’, Israel. Proceedings of the National Academy of Sciences USA 111: 10630-10635.

40.  Michalak K., Czesny S., Epifanio J., Snyder R.J., Schultz E.T., Velotta J.P., McCormick S.D., Brown B.L., Santopietro G., Michalak P. 2014. Beta-thymosin gene polymorphism associated with freshwater invasiveness of alewife (Alosa pseudoharengus). Journal of Experimental Zoology Part A: Ecological Genetics & Physiology 321: 233-240. [Cover]

41.  Michalak P. 2014. Evidence for maternal imprinting of 45S ribosomal RNA genes in Xenopus hybrids. Development Genes & Evolution 224: 125-128.

42.  Hübner S., Rashkovetsky E., Kim Y.B., Oh J.H., Michalak K., Weiner D., Korol A.B., Nevo E. & Michalak P. 2013. Genome differentiation of Drosophila melanogaster from a microclimate contrast in Evolution Canyon, Israel. Proceedings of the National Academy of Sciences USA 110: 21059-2164.

43.  Viladomiu M., Hontecillas R., Pedragosa M., Michalak P., Michalak K., Guerrant R., Roche J., Warren C. & Bassaganya-Riera J. 2012. Modulation of immune responses to Clostridium difficile by peroxisome proliferator-activated receptor gamma and miRNA-146b. Journal of Immunology 2012, 188.

44.  Zhang H, Wang H, Shepherd M, Wen K, Li G, et al. Probiotics and virulent human rotavirus modulate the transplanted human gut microbiota in gnotobiotic pigs. Gut Pathogens. 2014; 6:39.

45.  Huang YW, Dickerman AW, Piñeyro P, Li L, Fang L, et al. Origin, evolution, and genotyping of emergent porcine epidemic diarrhea virus strains in the United States. mBio. 2013; 4(5):e00737-13.

46.  Aulakh SS, Veilleux RE, Dickerman AW, Tang G, Flinn BS. Characterization and RNA-seq analysis of underperformer, an activation-tagged potato mutant. Plant Molecular Biology. 2014; 84(6):635-58.

47.  Yuan L., Zhi. W., Karyala, S., Vikesland, P.J., Chen, X., Zhang, J. Lead toxicity to the performance, viability, and community composition of activated sludge microorganisms, Environ Sci Tech. 2015 Jan 20;49(2):824-30.

48.  Cao, Z., Chen, C., He, B., Tan, K., & Lu, C. (2015). A microfluidic device for epigenomic profiling using 100 cells. Nature Methods. 2015 Oct;12(10):959-62

49.  Stepherd, ML, Swecker, WS, Jensen, RV, Ponder, MA.  Characterization of the fecal bacteria communities of forage-fed horses by pyrosequencing of 16S rRNA V4 gene amplicons. FEMS Microbiol Lett. 2012, 326:62-8.

50. Holliday, JA,. Zhou, L. Bawa, R, Zhang, M, Oubida, RW., Evidence for extensive parallelism but divergent genomic architecture of adaptation along altitudinal and latitudinal gradients in Populus trichocarpa. New Phytologist, 2015, doi: 10.1111/nph.13643. [Epub ahead of print].

51.  Manrique-Carpintero, NC, Tokuhisa, JG, Ginzberg, I, Holliday, JA, Veilleux, RE.  Sequence diversity in coding regions of candidate genese in the glycoalkaloid biosynthetic pathway of wild potato species. G3, 2013, 3:1467-79.

52.  Rodriques, RR, Pineda, RP, Barney, JN, Nilsen, ET., Barrett, JE, Williams, MA.  Plant invasions associated with change in root-zone microbial community structure and diversity. PLoS One, 2015 Oct 27;10(10):e0141424. doi: 10.1371/journal.pone.0141424. eCollection 2015.

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