- Methodology article
- Open Access
Genome-wide modeling of complex phenotypes in Caenorhabditis elegans and Drosophila melanogaster
© De et al.; licensee BioMed Central Ltd. 2013
Received: 14 February 2013
Accepted: 23 May 2013
Published: 28 August 2013
The genetic and molecular basis for many intermediate and end stage phenotypes in model systems such as C. elegans and D. melanogaster has long been known to involve pleiotropic effects and complex multigenic interactions. Gene sets are groups of genes that contribute to multiple biological or molecular phenomena. They have been used in the analysis of large molecular datasets such as microarray data, Next Generation sequencing, and other genomic datasets to reveal pleiotropic and multigenic contributions to phenotypic outcomes. Many model systems lack species specific organized phenotype based gene sets to enable high throughput analysis of large molecular datasets.
Results and discussion
Here, we describe two novel collections of gene sets in C. elegans and D. melanogaster that are based exclusively on genetically determined phenotypes and use a controlled phenotypic ontology. We use these collections to build genome-wide models of thousands of defined phenotypes in both model species. In addition, we demonstrate the utility of these gene sets in systems analysis and in analysis of gene expression-based molecular datasets and show how they are useful in analysis of genomic datasets connecting multigenic gene inputs to complex phenotypes.
Phenotypic based gene sets in both C. elegans and D. melanogaster are developed, characterized, and shown to be useful in the analysis of large scale species-specific genomic datasets. These phenotypic gene set collections will contribute to the understanding of complex phenotypic outcomes in these model systems.
Traditional experimentation in animal model systems such as the worm Caenorhabditis elegans and the fly Drosophila melanogaster often results in complex molecular and phenotypic outcomes. Frequently a targeted deletion or ectopic expression of a single gene product results in pleiotropic phenotypes. Similarly, broad high-throughput multiplex experimental strategies such as microarray based gene expression, RNA interference (RNAi) screens, or next-generation DNA and RNA sequencing, analyzing phenomena such as development, behavior, mating, diet, and life span, typically produce large datasets requiring complex analytical approaches.
Gene sets are collections of keyword terms with annotated genes derived from multiple sources of a priori information. They have been used in computational analysis of gene expression data [1–3] with the goal of identifying higher order relationships beyond simple gene list results, as well as in analysis of population based GWAS in humans [4, 5]. The most commonly used gene sets include those derived from GO annotations , biological pathways from KEGG  or BioCarta, expression modules, DNA binding sites, or other sources of molecular information [1, 3, 8]. Each collection of gene sets has its own unique qualities and features which are useful in different ways. For instance, KEGG emphasizes metabolic and biochemical pathways; GO annotations, while having some phenotypic content, emphasizes molecular function, cellular component, and biological processes, while MSigDB  emphasizes gene expression signatures. This information is often closely related, or “proximal” to gene and molecular function, rather than more “distal” information regarding phenotypic outcomes and disease susceptibility. Recently, phenotype based gene sets have been derived exclusively from genetically determined phenotypic associations for mouse phenotypes and common human disease [9, 10], resulting in gene sets for specific phenotypes, organized by a structured systematic ontology.
Here, we present gene sets for worm and fly, which use the structured ontology found in the Worm Phenotype Ontology from the C. elegans database - WormBase  and phenotypic descriptions for D. melanogaster found in FlyBase . These gene sets are derived from information on gene-phenotype relationships based on genetically determined phenotypes. We use these collections in large scale phenotypic modeling in worms and flies and demonstrate their utility in complex analysis in multiple ways, including analysis of gene expression datasets representing complex phenotypic and biological phenomena in both C. elegans and D. melanogaster. In this way, we integrate large scale genome analysis with large scale phenotypic analysis in these two model systems.
Derivation of worm gene sets
The worm gene sets presented here are derived from two lists of genes and assigned phenotypes provided by Gary Schindelman and Paul Sternberg as a component of the Worm Phenotype Ontology . These two lists originated from information curated from RNAi experiments and genetic variations (VAR) as archived in WormBase .
Two worm gene set files (CE- RNAi-GS and CE-VAR-GS) were produced by parsing each gene list separately into non-redundant lists of unique phenotypic terms with all genes assigned to their corresponding phenotypic terms. This produced two non-redundant gene set files containing 850 and 1109 gene sets for RNAi and VAR, respectively. In addition, we developed a master worm file by combining the original RNAi and VAR gene lists into a combined file (CE-Combined-GS) containing 1,385 non-redundant phenotypes and their associated gene sets.
Derivation of fly gene sets
The Drosophila gene sets described here are derived from phenotypic data provided in FlyBase (see Methods). A file containing 259,162 phenotypic descriptions with assigned Drosophila genes was collapsed and parsed resulting in a non-redundant gene set file of 11,999 unique phenotypic terms with annotated genes. This file named DM-narrow-GS was used for systems biology and gene expression analysis.
Selected Phenotype gene sets
Large gene sets
AC7.1(tag-49), AC7.10, AC7.13, AC7.19, AC7.68, AC8.6, AH6.5, B0001.2, B0025.2 9(csn.2), B0025.5, etc…
AC8.6, B0025.1(vps-34), B0035.10(his-45), B0035.11, B0035.12, B0035.7(his-47), B0035.8(his-48), B0205.6, B0238.11, B0250.1, etc…
AC3.7, AC8.6, B0024.4, B0025.1(vps-34), B0025.2, B0025.5, B0025.6, B0035.10(his-45), B0035.11, B0035.12, etc…
AH6.5 (mex-6), B0025.1(vps-34), B0035.11, B0035.12, B0035.14(dn-j1), B0035.15, B0035.7(his-47), B0035.8(his-48), B0035.9(his-46), B0207.4(air-2), etc…
AC7.1(tag-49), AC7.10, AC7.13, AC7.19, AC7.68, AC8.6, B0024.14, B0025.2, B0025.5, B0035.10(his-45), etc…
reduced brood size
AC8.1, AC8.2, B0035.10(his-45), B0035.11, B0041.4(rpl-4), B0205.6, B0207.4(air-2), B0252.9, B0261.2(let-363), B0261.4, etc…
B0035.11, B0035.8(HIS-48), B0041.4(RPL-4), B0205.6, B0212.4, B0284.1, B0284.6, B0285.1, B0286.4(NTL-2), B0304.1(HLH-1), etc…
AC7.11, AC7.15, AC7.18, AC7.2(soc-2), AC7.22, AC7.29, AC7.33, AC7.4, AC7.6, AC7.65, etc…
Intermediate gene sets
cytokinesis fails early emb
B0207.4(air-2), B0273.2(puf-7), C01F6.3, C03C10.3(rnr-2), C07H6.5(cgh-1), C08B11.1(zyzg-11), C09G4.3(cks-1), C17G10.4(CDC-14), C25A1.9(rsa-1), C32E8.8(ptr-2), etc…
cell cycle slow early emb
C03C10.3(rnr-2), C08B11.1(zyzg-11), C14B9.4(plk-1), C26D10.2(hel-1), C27A2.3(ify-1), C30C11.2(rpn-3), C33H5.15(sgo-1), C40H5.6, C40H5.8, C47E12.5(uba-1), etc…
pharyngeal pumping reduced
B0348.4(egl-8), B0365.3(eat-6), B0412.2(daf-7), B0495.4(nhx-2), C02C6.1(dyn-1), C05D2.1(daf-4), C09B7.1(ser-7), C09B7.10, C09B7.12, C09B7.9, etc…
pronuclear size defective early emb
B0035.12, C08B11.1(zyzg-11), C08B6.9, C26D10.1(ran-3), C27A2.3(ify-1), C28C12.2, C37A2.4(cye-1), C38D4.3(mel-28), C40H5.6, C40H5.8, etc…
bag of worms
B0348.4(egl-8), B0412.2(daf-7), C04A2.3(egl-27), C04G2.7(egl-38), C05D9.5(ife-4), C08C3.1(egl-5), C26E6.8(ula-1), C30A5.7(uno-86), C44B12.2(ost-1), C46F4.1, etc…
exaggerated asynchrony early emb
C03C10.3(rnr-2), C25D7.6(mcm-3), C26D10.1(ran-3), C28C12.2, C29A12.3(lig-1), C38D4.3(mel-28), C39E9.13(rfc-3), C40H5.6, C40H5.8, C54G10.2(rfc-1), etc…
organism osmotic stress response var.
B0218.3(pmk-1), C12C8.1(hsp-70), C32E12.3(osr-1), C53B4.12, C53D6.18, F07C6.7, F10D2.9(fat-7), F11C7.5(osm-11), F19H8.1(tps-2), F38E11.1(hsp-12.3), etc…
dead eggs laid
C09D4.5(rpl-19), C27A2.2(rpl-22), C36E8.5(tbb-2), C47B2.3(tba-2), C53A5.1(ril-1), C54C6.2(ben-1), F25B5.4(ubq-1), F26D10.3(hsp-1), F26E4.8(tba-1), F44F4.11(tba-4), etc…
Small gene sets
neuron function reduced
F36F2.5(tax-2), F55A8.2(egl-4), K03A11.3(ceh-28), K03A11.8, ZC416.8(unc-17), ZC84.2(tax-4), ZK1290.18, ZK1290.2(tph-1)
neuron morphology variant
C10A4.8(mnm-2), C35C5.4(mig-2), C44B11.3(mec-12), F28D1.10(gex-3), K10G9.3(pad-2), T01E8.4, Y51H4A.3, ZK154.3(mec-7)
pheromone induced dauer form. enhan
C38C3.5(unc-60), F02E8.6(ncr-1), F02E8.9, F55A8.2(egl-4), T20B5.3(oga-1), Y44A6D.4(sdf-9), Y6B3B.11(hsd-1)
programmed cell death variant
C07H6.7(lin-39), C09G4.1(hyl-1), F31E3.1(ceh-20), T07C4.8(ced-9), T12F5.4(lin-59), T28F12.2(unc-62), Y6B3B.10(lag-1)
cell division slow
C26D10.1(ran-3), C29E4.3(ran-2), F26B1.3(ima-2), F28B3.8(imb-1), K01G5.4(ran-1), ZK328.5(npp-10)
ectopic neurite outgrowth
B0285.5(hse-5), C35C5.4(mig-2), C39F7.2, F41C6.1(unc-6), T19B4.7(unc-40), T24B8.6(hlh-3)
dauer cuticle variant
C47G2.1(cut-1), C47G2.15, F22B5.3(cut-3), M142.2(cut-6), ZC328.1
endosome biogenesis variant
F49E7.1(rme-6), F58G6.1(amph-1), W06B4.3(vps-18), Y39A1A.5(rabx-5), Y49E10.11(tat-1)
Large gene sets
arc, abb, abr, ac, Act5C, ade2, ade3, amb, aop, Appl, etc…
abd-A, Abd-B, abt, ac, ade2, amb, ano, aop, ap, ar, etc…
abd-A, Abd-B, abt, abw, ac, ade2, ade3, al, aop, ap, etc…
ade2, ade3, amb, aop, arm, bi, bo, bos, br, brb, etc…
a, Abd-B, Abl, abr, abt, ald, amx, aop, apx, arm, etc…
Abl, Ace, acj6, Appl, Arr2, bas, baz, bi, bsk, bss, etc…
ap, cad, car, Cat, cm, comt, dnc, dor, ecd, EcR, etc…
body color defective
Abd-B, abt, amb, asx, b, bi, Bkd, br, cal, crm, etc…
Intermediate gene sets
arm, bam, cg, cos, Dl, ds, eyg, gd, ft, ImpL2, etc…
aop, arm, bi, dpp, ds, ena, ft, gt, Hsc70-4, L, etc…
agn, cab, cbd, ccb, ccd, Ddc, dnc, eag, Fas2, G-salpha60A, etc…
increased cell size
brm, fkh, Hsc70-4, swm, mod, phl, Ras85D, shi, stg, Egfr, etc…
arm, arr, ase, bi, brm, clm, Dr, dsh, eg, fu, etc…
abd-A, Abd-B, ac, ap, Bx, Pka-C1, Dl, dpp, Dr, ds, etc…
cell death defective
DNaseII, dor, dsh, EcR, dco, numb, Ras85D, rst, W, Top1, etc…
ImpL2, l(2)gl, phl, Ras85D, rl, tkd, tor, tsh, gig, CycD, etc…
Small gene sets
bi, rst, so, sim, ato, tutl, Scer\GAL4, elav
CNS glial cell
E(z), sws, gcm, Scer\GAL4, spdo, hkb, vnd
slou, N, Rac1, insc, Scer\GAL4, Hsap\CDKN1A, tw
sex comb tooth
Pc, Scr, ph-p, Scer\GAL4, KG01932, 5-SZ-3716, Zzzz\Aobl-tra
abdominal 3 seg. border muscle
if, numb, mys, insc, Tig, Scer\GAL4
ed, pim, tkv, ct, Scer\GAL4, exo84
dnc, Wnt5, Cdc42, Scer\GAL4, drl, Drl-2
bsk, kay, pnr, Scer\GAL4, park, puc
General uses of phenotype based gene sets in both worm and fly
As described here, a single gene set is essentially a single phenotypic term followed by a single row of genes that have been associated with that phenotype. A collection of gene sets consists of a list of phenotypic terms with their corresponding gene sets. Gene sets can be used individually, as a collection, or compared across collections in a number of ways including network analysis, genome-wide model representations, hierarchical clustering, gene set analysis (GSA) of microarray data, and principal component analysis (PCA) of gene set values; among others. A property of this collection of gene sets is that they describe complex intermediate and end stage phenotypes as opposed to molecular function or lists of coordinately regulated genes. They can be used in a variety of bioinformatics applications to reveal higher order or emergent biological and phenotypic relationships and to provide insight into the biological relevance of complex molecular datasets.
Genome-wide phenotypic modeling in worms and flies
Phenotype Gene Set Analysis (GSA) of microarray data and Principal Components Analysis (PCA) of gene sets
Here we describe genome-wide phenotypic modeling using gene sets based on gene-phenotypic assignments in C. elegans and D. melanogaster. Unlike previous gene set collections such as KEGG, GO, MSigDB, in these and other species, every gene in every gene set described here is based on genetic evidence contributing to each specific phenotype. Although very useful, these gene sets should be considered a first generation. They may not be complete. Some may describe certain phenotypes in different developmental contexts, or in particular applications and not in others. In addition, many subtleties and details were not included in deriving these gene sets including penetrance of different alleles, strain differences, and environmental modifiers. Moreover, these gene sets may produce different results depending on the statistical algorithms used in complex analysis.
However, we have demonstrated these gene sets can be used to identify complex higher order biological and genetic relationships through network analysis, whole genome phenotypic modeling, and analysis of complex molecular datasets. They will help elucidate complex multigenic relationships between genes and phenotypes in worms and flies in many experimental and biological contexts and will provide a bridge for phenotypic comparisons between model and intermediate species.
Derivation of phenotypic gene sets
Phenotype-gene lists obtained from WormBase on 4/24/11 were titled RNAi and VAR. RNAi, consisted of 34,433 gene phenotype pairs having 7,289 unique genes and 850 unique phenotypes. These phenotypes were the results of observations of phenotypes from knockdown of the gene products (RNAi experiments). The list VAR contained 8,440 records, having 2,165 unique genes, and 1,109 unique phenotypes and was the result of observations of phenotypes from genetic mutations as deposited in WormBase. The overlap between each file consists of 1,410 genes and 237 phenotypes.
Phenotype gene set files were created by parsing the original gene lists into non-redundant phenotype lists with annotated genes using a custom Perl script as previously described . This was done for RNAi and VAR independently, as well as combined to create the gene set files; CE-RNAi-GS 7-26-11, CE-VAR-GS 7-26-11, and CE-Combined-GS 7-28-11. The resultant individual Phenotype Gene set names are identical to the Phenotype descriptors found in the original WormBase Phenotype file. These files can be downloaded here: http://www.grc.nia.nih.gov/branches/rrb/dna/index/Worm-fly_gene_sets_5-9-12.html.
Phenotypes and gene assignments were obtained from FlyBase on 9-11-11 at this web address: http://FlyBase.org/static_pages/downloads/FB2011_07/alleles/allele_phenotypic_data_fb_2011_07.tsv.gz. This file began with 259,162 phenotypic descriptions with assigned Drosophila genes. Redundant phenotype-gene combinations were removed resulting in a list of 154,428 unique phenotype-single gene pairs. Parsing of this file resulted in a non-redundant gene set file of 11,999 unique phenotypic terms with annotated genes. The resultant individual Phenotype Gene set names are identical to the Phenotype descriptors found in the original FlyBase Phenotype file. This Phenotype gene set file named DM-narrow-GS 9-7-2011, can be downloaded here: http://www.grc.nia.nih.gov/branches/rrb/dna/data/worm-fly/DM-narrow-GS_9-7-2011.txt.
Gene set nomenclature
It should be noted that nomenclature of many phenotype gene sets in both worm and fly often have a directionality in the name which may or may not be relevant to any given microarray or other analysis. Please see Additional file 9: S7 for an explanation of directionality in gene set nomenclature and interpretation in their use.
Networks for C. elegans and D. melanogaster were produced using Ingenuity Pathway Analysis (IPA) (Ingenuity® Systems, http://www.ingenuity.com). Using the “life_span_variant” gene set in C. elegans generated on 7-26-2011, and the “long_lived” gene set in D. melanogaster generated on 12-07-2011. The input and output files can be found here for C. elegans (Additional file 10: Table S5) and D. melanogaster (Additional file 11: Table S6).
Genome-wide phenotypic modeling
Genome-wide dendrograms were produced by a unique method similar to phylogenetic classification as previously described . Briefly, the distance between each phenotypic gene set was calculated by pairwise comparison of every gene set pair by finding the number of common genes between each pair and dividing that number by the number of genes in the smallest group of the pair, resulting in a correlation value between 1 and 0 for each pair. This was done for all gene sets to produce a distance matrix. This number was then subtracted from 1because if two lists are identical (100 % match) then the resultant distance should be 0. This is represented as:
Where: C k : Genes in each disease set (where k = i,j) ; N(C k ): Number of genes in each disease set (where k = i,j) ; dij is the pairwise distance ; i,j: index of genes in each disease set where; i = 1,2,3,………,n ; j = 1,2,3,………,m.
where D is the observed distance between gene sets i and j and d is the expected distance, computed as the sum of the lengths of the segments of the tree from gene set i to gene set j. The quantity n is the number of times each distance has been replicated. In simple cases n is taken to be one. If n is chosen more than 1, the distance is then assumed to be a mean of those replicates. The power P is what distinguished between the Fitch and Neighbor-Joining methods. For the Fitch- Margoliash method P is 2.0 and for Neighbor-Joining method it is 0.0. The resulting coefficient matrix file was displayed using the Phylodraw graphics program .
Gene set analysis
This analysis used the Disease/Phenotype WEB-PAGE GSA web tool using the PAGE algorithm  with the CE-Combined-GS gene set file excluding gene sets containing over 500 and less than 3 genes. Briefly, for each gene set a Z score was computed as, In which the phenotype index i = 1,2,…,K; where K is the total number of the disease phenotypes we included in our data set; nI is the number of genes in the sub-group of phenotype i in the current sample array; σA is the standard deviation of the current gene expression changes of the sample. Diff(i): is the difference between the mean value of gene expression changes in the subgroup disease phenotype (i) (GCI ) and the mean value of the gene expression changes on the whole sample (GCA) i.e. . The empirical p-value of the disease phenotype i changes is described by: in which Φ(x) is the standard normal distribution function with the variable as X = DIFFI/σ(DIFFFI). σ(DIFFI) is the standard deviation of the difference for gene expression changes between phenotype subgroup (i) and the whole array σI is the standard deviation of the average gene expression changes in the disease phenotype (i). NA is the total number of genes in the whole sample set. The plots were drawn with R-statistical programming language (R Development Core Team 2005) using either calculated or absolute z-score values.
Principal components analysis
Principal components analysis was performed on the gene set Z values using DIANE 8.0 a JMP based software package (http://www.grc.nia.nih.gov/branches/rrb/dna/diane_software.pdf) based on the Singular Value Decomposition (SVD) function in JMP 9.0. In short, the data was organized as m × n matrix where m is the different samples (columns) and n is gene set Z-values (rows), mean of each row was subtracted and SVD was calculated using JMP’s in-built SVD function as illustrated in this document: http://www.cs.princeton.edu/picasso/mats/PCA-Tutorial-Intuition_jp.pdf and also used in this script: http://abs.cit.nih.gov/MSCLtoolbox.
The complete C. elegans and D. melanogaster gene set files are available at this address: http://www.grc.nia.nih.gov/branches/rrb/dna/index/Worm-fly_gene_sets_5-9-12.html.
The authors would like to thank Gary Schindelman and Paul Sternberg from WormBase for providing gene-phenotype files and Dr Elin Lehrmann for critical reading of the manuscript. This research was supported entirely by the Intramural Research Program of the NIH, National Institute on Aging.
- Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003, 34 (3): 267-273. 10.1038/ng1180.View ArticlePubMedGoogle Scholar
- Kim SY, Volsky DJ: PAGE: parametric analysis of gene set enrichment. BMC Bioinforma. 2005, 6: 144-10.1186/1471-2105-6-144.View ArticleGoogle Scholar
- Nam D, Kim SY: Gene-set approach for expression pattern analysis. Brief Bioinform. 2008, 9 (3): 189-197. 10.1093/bib/bbn001.View ArticlePubMedGoogle Scholar
- Nam D, Kim J, Kim SY, Kim S, 4.: GSA-SNP: a general approach for gene set analysis of polymorphisms. Nucleic Acids Res. 2010, 38: 749-754. 10.1093/nar/gkq428. Web Server issueView ArticleGoogle Scholar
- Wang L, Jia P, Wolfinger RD, Chen X, Zhao Z: Gene set analysis of genome-wide association studies: methodological issues and perspectives. Genomics. 2011, 98 (1): 1-8.View ArticlePubMedGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25 (1): 25-29.PubMedGoogle Scholar
- Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012, 40: 109-114. Database issueView ArticleGoogle Scholar
- Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005, 102 (43): 15545-15550. 10.1073/pnas.0506580102.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang Y, De S, Garner JR, Smith K, Wang SA, Becker KG: Systematic analysis, comparison, and integration of disease based human genetic association data and mouse genetic phenotypic information. BMC Med Genomics. 2010, 3: 1-10.1186/1755-8794-3-1.PubMed CentralView ArticlePubMedGoogle Scholar
- De S, Zhang Y, Garner JR, Wang SA, Becker KG: Disease and phenotype gene set analysis of disease-based gene expression in mouse and human. Physiol Genomics. 2010, 42A (2): 162-167. 10.1152/physiolgenomics.00008.2010.PubMed CentralView ArticlePubMedGoogle Scholar
- Yook K, Harris TW, Bieri T, Cabunoc A, Chan J, Chen WJ, Davis P, Dela Cruz N, Duong A, Fang R: WormBase 2012: more genomes, more data, new website. Nucleic Acids Res. 2012, 40: 735-741. 10.1093/nar/gkr954. Database issueView ArticleGoogle Scholar
- McQuilton P, St Pierre SE, Thurmond J: FlyBase 101--the basics of navigating FlyBase. Nucleic Acids Res. 2012, 40: 706-714. 10.1093/nar/gkr1030. Database issueView ArticleGoogle Scholar
- Schindelman G, Fernandes JS, Bastiani CA, Yook K, Sternberg PW: Worm Phenotype Ontology: integrating phenotype data within and beyond the C. elegans community. BMC Bioinforma. 2011, 12: 32-10.1186/1471-2105-12-32.View ArticleGoogle Scholar
- Harris TW, Antoshechkin I, Bieri T, Blasiar D, Chan J, Chen WJ, De la Cruz N, Davis P, Duesbury M, Fang R: WormBase: a comprehensive resource for nematode research. Nucleic Acids Res. 2010, 38 (Database issue): 463-467.View ArticleGoogle Scholar
- Youngman MJ, Rogers ZN, Kim DH: A decline in p38 MAPK signaling underlies immunosenescence in Caenorhabditis elegans. PLoS Genet. 2011, 7 (5): e1002082-10.1371/journal.pgen.1002082.PubMed CentralView ArticlePubMedGoogle Scholar
- Golden TR, Hubbard A, Dando C, Herren MA, Melov S: Age-related behaviors have distinct transcriptional profiles in Caenorhabditis elegans. Aging Cell. 2008, 7 (6): 850-865. 10.1111/j.1474-9726.2008.00433.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Wood JG, Hillenmeyer S, Lawrence C, Chang C, Hosier S, Lightfoot W, Mukherjee E, Jiang N, Schorl C, Brodsky AS: Chromatin remodeling in the aging genome of Drosophila. Aging Cell. 2010, 9 (6): 971-978. 10.1111/j.1474-9726.2010.00624.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Felsenstein J: An alternating least squares approach to inferring phylogenies from pairwise distances. Syst Biol. 1997, 46 (1): 101-111. 10.1093/sysbio/46.1.101.View ArticlePubMedGoogle Scholar
- Fitch WM, Margoliash E: Construction of phylogenetic trees. Science. 1967, 155 (3760): 279-284. 10.1126/science.155.3760.279.View ArticlePubMedGoogle Scholar
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