<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/26629?offset=1180 https://bioinformaticsonline.com/bookmarks/view/20471/bioinformatics-scripts Thu, 22 Jan 2015 22:29:39 -0600 https://bioinformaticsonline.com/bookmarks/view/20471/bioinformatics-scripts <![CDATA[Bioinformatics Scripts]]> Some of the useful bioinformatics scripts.

For example ... contig-stats.pl is a Perl script that will automatically describe features of a sequence assembly.

http://milkweedgenome.org/?q=scripts

Address of the bookmark: http://milkweedgenome.org/?q=scripts

]]> Jit https://bioinformaticsonline.com/blog/view/43999/tools-for-differential-expression-analysis Tue, 08 Nov 2022 03:40:33 -0600 https://bioinformaticsonline.com/blog/view/43999/tools-for-differential-expression-analysis <![CDATA[Tools for Differential expression analysis]]> apeglm - https://bioconductor.org/packages/release/bioc/html/apeglm.html

ashr - https://github.com/stephens999/ashr, https://cran.r-project.org/web/packages/ashr/index.html

consensusDE - https://bioconductor.org/packages/release/bioc/html/consensusDE.html

DESeq2 - https://bioconductor.org/packages/release/bioc/html/DESeq2.html

edgeR - https://bioconductor.org/packages/release/bioc/html/edgeR.html

limma - https://kasperdanielhansen.github.io/genbioconductor/html/limma.html  https://bioconductor.org/packages/release/bioc/html/limma.html

MetaCycle - https://cran.r-project.org/web/packages/MetaCycle/index.html, https://github.com/gangwug/MetaCycle

RUVSeq - https://bioconductor.org/packages/release/bioc/html/RUVSeq.html

SARTools - https://github.com/PF2-pasteur-fr/SARTools

tximport - https://github.com/mikelove/tximport

 

]]> Abhi https://bioinformaticsonline.com/pages/view/34221/alignment-free-sequence-comparison-tools-available-for-next-generation-sequencing-data-analysis Tue, 07 Nov 2017 05:33:33 -0600 https://bioinformaticsonline.com/pages/view/34221/alignment-free-sequence-comparison-tools-available-for-next-generation-sequencing-data-analysis <![CDATA[Alignment-free sequence comparison tools available for next-generation sequencing data analysis]]>

kallisto

Transcript abundance quantification from RNA-seq data (uses pseudoalignment for rapid determination of read compatibility with targets)

Software (C++)

https://pachterlab.github.io/kallisto/

Sailfish

Estimation of isoform abundances from reference sequences and RNA-seq data (k-mer based)

Software (C++)

http://www.cs.cmu.edu/~ckingsf/software/sailfish/

Salmon

Quantification of the expression of transcripts using RNA-seq data (uses k-mers)

https://combine-lab.github.io/salmon/

RNA-Skim

RNA-seq quantification at transcript-level (partitions the transcriptome into disjoint transcript clusters; uses sig-mers, a special type of k-mers)

Software (C++)

http://www.csbio.unc.edu/rs/

Variant calling

ChimeRScope

Fusion transcript prediction using gene k-mers profiles of the RNA-seq paired-end reads

Software (Java)

https://github.com/ChimeRScope/ChimeRScope/wiki

FastGT

Genotyping of known SNV/SNP variants directly from raw NGS sequence reads by counting unique k-mers

Software (C)

https://github.com/bioinfo-ut/GenomeTester4/

Phy-Mer

Reference-independent mitochondrial haplogroup classifier from NGS data (k-mer based)

Software (Python)

https://github.com/danielnavarrogomez/phy-mer

LAVA

Genotyping of known SNPs (dbSNP and Affymetrix's Genome-Wide Human SNP Array) from raw NGS reads (k-mer based)

Software (C)

http://lava.csail.mit.edu/

MICADo

Detection of mutations in targeted third-generation NGS data (can distinguish patients’ specific mutations; algorithm uses k-mers and is based on colored de Bruijn graphs)

Software (Python)

http://github.com/cbib/MICADo

General mapper

Minimap

Lightweight and fast read mapper and read overlap detector (uses the concept of “minimazers”, a special type of k-mers)

Software (C)

https://github.com/lh3/minimap

Assembly

De novo genome assembly

MHAP

Produces highly continuous assembly (fully resolved chromosome arms) from third-generation long and noisy reads (10 kbp) using a dimensionality reduction technique MinHash

Software (Java)

https://github.com/marbl/MHAP

Miniasm

Assembler of long noisy reads (SMRT, ONT) using the Overlap-Layout Consensus (OLC) approach without the necessity of an error correction stage (uses minimap)

Software (C)

https://github.com/lh3/miniasm

LINKS

Scaffolding genome assembly with error-containing long sequence (e.g., ONT or PacBio reads, draft genomes)

Software (Perl)

https://github.com/warrenlr/LINKS/

Read clustering

afcluster

Clustering of reads from different genes and different species based on k-mer counts

Software (C++)

https://github.com/luscinius/afcluster

QCluster

Clustering of reads with alignment-free measures (k-mer based) and quality values

Software (C++)

http://www.dei.unipd.it/~ciompin/main/qcluster.html

Reads error correction

Lighter

Correction of sequencing errors in raw, whole genome sequencing reads (k-mer based)

Software (C++)

https://github.com/mourisl/Lighter

QuorUM

Error corrector for Illumina reads using k-mers

Software (C++)

https://github.com/gmarcais/Quorum

Trowel

Software (C++)

https://sourceforge.net/projects/trowel-ec/

Metagenomics

Assembly-free phylogenomics

AAF

Phylogeny reconstruction directly from unassembled raw sequence data from whole genome sequencing projects; provides bootstrap support to assess uncertainty in the tree topology (k-mer based)

Software (Python)

https://github.com/fanhuan/AAF

kSNP v3

Reference-free SNP identification and estimation of phylogenetic trees using SNPs (based on k-mer analysis)

Software (C)

https://sourceforge.net/projects/ksnp/files/

NGS-MC

Phylogeny of species based on NGS reads using alignment-free sequence dissimilarity measures d2* and d2 S under different Markov chain models (using k-words)

R package

http://www-rcf.usc.edu/~fsun/Programs/NGS-MC/NGS-MC.html

Species identification/taxonomic profiling

CLARK

Taxonomic classification of metagenomic reads to known bacterial genomes using k-mer search and LCA assignment

Software (C++)

http://clark.cs.ucr.edu/

FOCUS

Reports organisms present in metagenomic samples and profiles their abundances (uses composition-based approach and non-negative least squares for prediction)

Web service Software (Python)

http://edwards.sdsu.edu/FOCUS/

GSM

Estimation of abundances of microbial genomes in metagenomic samples (k-mer based)

Software (Go)

https://github.com/pdtrang/GSM

Mash

Species identification using assembled or unassembled Illumina, PacBio, and ONT data (based on MinHash dimensionality-reduction technique)

Software (C++)

https://github.com/marbl/mash

Kraken

Taxonomic assignment in metagenome analysis by exact k-mer search; LCA assignment of short reads based on a comprehensive sequence database

Software (C++)

https://ccb.jhu.edu/software/kraken/

LMAT

Assignment of taxonomic labels to reads by k-mers searches in precomputed database

Software (C++/Python)

https://sourceforge.net/projects/lmat/

stringMLST

k-mer-based tool for MLST directly from the genome sequencing reads

Software (Python)

http://jordan.biology.gatech.edu/page/software/stringMLST

Taxonomer

k-mer-based ultrafast metagenomics tool for assigning taxonomy to sequencing reads from clinical and environmental samples

Web service

http://taxonomer.iobio.io/

Other

d2-tools

Word-based (k-tuple) comparison (pairwise dissimilarity matrix using d2S measure) of metatranscriptomic samples from NGS reads

Software (Python/R)

https://code.google.com/p/d2-tools/

VirHostMatcher

Prediction of hosts from metagenomic viral sequences based on ONF using various distance measures (e.g., d2)

Software (C++)

https://github.com/jessieren/VirHostMatcher

MetaFast

Statistics calculation of metagenome sequences and the distances between them based on assembly using de Bruijn graphs and Bray–Curtis dissimilarity measure

Software (Java)

https://github.com/ctlab/metafast

]]> Abhimanyu Singh https://bioinformaticsonline.com/opportunity/view/20672/jrfra-structuralcomputational-biology-at-icgeb Thu, 29 Jan 2015 11:52:40 -0600 <![CDATA[JRF/RA Structural/Computational Biology at ICGEB]]> Research Associate and JRF positions in the Structural and Computational Biology Group starting 1st March 2015. Collaborative projects include work on:

a) bioinformatics, systems and computational biology
b) malaria
c) drug discovery
d) genomics
e) microbiology
f) metabolic disorders
g) molecular medicine

Eligibility: Applicants must have one of the following :

1) INSPIRE award for undertakig either PhD or Postdoctoral research;
2) SPM award for PhD;
3) JRF for pursuing PhD from CSIR/DBT/ICMR

Interest and experience in Biochemistry/Bioinformatics/Biophysics/ Chemistry/Genomics/Molecular Biology/ is essential.

Submit curriculum vitae to sb.icgeb@gmail.com by 20 February 2015

]]> https://bioinformaticsonline.com/bookmarks/view/36516/metassembler-merging-and-optimizing-de-novo-genome-assemblies Tue, 08 May 2018 04:52:33 -0500 https://bioinformaticsonline.com/bookmarks/view/36516/metassembler-merging-and-optimizing-de-novo-genome-assemblies <![CDATA[Metassembler: merging and optimizing de novo genome assemblies]]> Metassembler combines multiple whole genome de novo assemblies into a combined consensus assembly using the best segments of the individual assemblies.

Genome assembly projects typically run multiple algorithms in an attempt to find the single best assembly, although those assemblies often have complementary, if untapped, strengths and weaknesses. We present our metassembler algorithm that merges multiple assemblies of a genome into a single superior sequence. 

Address of the bookmark: https://sourceforge.net/projects/metassembler/?source=directory

]]> Rahul Nayak https://bioinformaticsonline.com/opportunity/view/21538/senior-research-fellow-at-all-india-institute-of-medical-sciences-aiims-delhi-delhi-delhi Wed, 11 Mar 2015 03:06:10 -0500 <![CDATA[SENIOR RESEARCH FELLOW at All India Institute of Medical Sciences (AIIMS Delhi) - Delhi, Delhi]]> Applications are invited from eligible candidates for the following temporary post in an ICMR funded Research Project entitle “An Investigation to find out reasons for Phenotypic Heterogeneity/Variability in 22q11.2 Microdeletion Syndrome” in Department of Reproductive Biology, AIIMS, New Delhi PI: Dr. Ashutosh Halder, Professor, Department of Reproductive Biology

Name of the post: Senior Research Fellow (SRF)
Duration: 2 year
Salary: Rs. 28000/- per month + 30% HRA
Eligibility: MSc (life sciences) with 2 years research experience, NET/GATE qualified
Desirable: Experience in the field of Genomics, Epigenomics & Bioinformatics
SELECTION PROCEDURE FOR ALL INDIA INSTITUTE OF MEDICAL SCIENCES (AIIMS DELHI) – SENIOR RESEARCH FELLOW POST:

Candidates can apply on or before 15/03/2015
No Detailed information about the selection process is mentioned in the recruitment notification
HOW TO APPLY FOR SENIOR RESEARCH FELLOW VACANCY IN ALL INDIA INSTITUTE OF MEDICAL SCIENCES (AIIMS DELHI):

Deadline: 15.03.15 Submit your C.V in Room No. 2099 (Molecular Cytogenetics Lab), 2nd floor, Reproductive Biology, All India Institute of Medical Sciences, New Delhi-110029 or Email CV to: ashutoshhalder@gmail.com Your CV should include the details of your work experience & degrees along with two references with e-mail and contact number Only 10 shortlisted (on merit) candidates will be invited for interview. No TA/DA will be applicable for the same

]]> https://bioinformaticsonline.com/blog/view/38238/list-of-motif-discovery-tools Tue, 20 Nov 2018 03:54:26 -0600 https://bioinformaticsonline.com/blog/view/38238/list-of-motif-discovery-tools <![CDATA[List of motif discovery tools !]]>
In genetics, a sequence motif is a nucleotide or amino-acid sequence pattern that is widespread and has, or is conjectured to have, a biological significance. For proteins, a sequence motif is distinguished from a structural motif, a motif formed by the three-dimensional arrangement of amino acids which may not be adjacent.
 
Following are the list of tools for motif discovery:
 

Perform secondary structure predictions on protein sequences.

Find binding specificity information about DNA-protein complexes.

Find information about the binding specificity of DNA-binding proteins.

Use this web-based sequence motif visualization system to display sequence motif information in its appropriate three-dimensional (3D) context.

Protein function prediction and annotation in an integrated environment powered by web service.

Use to predict function from de novo protein sequences.

Search for short active protein sequences with demonstrated biological activities.

Search for ungapped segments corresponding to the most highly conserved regions of proteins.

Identify and measure surface accessible pockets as well as interior inaccessible cavities, for proteins and other molecules.

To search for catalytic residue annotation for enzymes in the Protein Data Bank.

Predict protein function using Gene Ontology.

Automatically calculate evolutionary conservation scores of key amino acid residues and map them on protein structures.

Mine the protein structure space.

Predict short linear motifs (3-8 residues) in a set of protein sequences.

A web client for visualizing protein sequence feature information using DAS.

Identify the domain architecture within a protein sequence.

Predict functional sites in eukaryotic proteins.

Use a collection of tools for protein analyses.

Predict potential protein post-translational modifications and find potential single amino acid substitutions in peptides.

Search for information related to the catalytic mechanisms of enzymes.

An integrated feature-based function prediction server for vertebrate proteomes.

Identify the closest matching PRINTS sequence motif fingerprints in a protein sequence.

Search for functional annotation of important sites in proteins with known structures.

Produce 3D conformations of small drug compounds.

A database presenting experiment-based results in human proteomics.

Conduct exhaustive intermediate profile searches of a set of homologous protein sequences.

Design protein mutations in site-directed mutagenesis.

Annotate protein using this integrated annotation resource.

Identify protein family (and DNA) domains, patterns, motifs, protein families, and functional sites.

Interactive forecasting of protein interaction hot spots.

Database containing pairs of structural analogs and their alignments.

Find sequence patterns in DNA and protein sequences.

A web server for knowledge-based modeling of protein-peptide complexes, specifically peptides in complex with major histocompatibility complex (MHC) proteins and kinases.

Find structural segments or motifs for protein structures.

Visualize protein sequence motifs on the 3D protein structures.

Find presence of any known protein motif (Prosite and Pfam) in a protein sequence.

Recognize spatial chemical binding patterns common to a set of protein structures.

Analyze proteins for the presence of N-terminal N-myristoylation site.

Find the presence of N-Glycosylation sites in human proteins.

Find the presence of O-GalNAc (mucin type) glycosylation sites in mammalian proteins.

Analyze eukaryotic proteins for the presence of serine, threonine and tyrosine phosphorylation sites.

Find possible kinase specific phosphorylation sites in eukaryotic proteins.

Predict cleavage sites at basic amino acid locations in neuropeptide precursor sequences.

Find information about patented nucleotide and protein sequences.

Search for information about glycoproteins with O-linked and C-linked glycosylation sites.

Find information about protein sequence annotations.

A server to predict protein active site residues.

Search for structural and functional information on the protein functional sites.

Search 3D protein fragments similar in structure to known active, binding and posttranslational modification sites.

Conduct genome wide functional and structural analysis.

Search for phosphorylation data of any protein of interest.

Search for information on prokaryotic proteins that undergo serine, threonine, or tyrosine phosphorylation.

Determine correct names for proteins.

Web application for predicting protein disorder by using physicochemical features and reduced amino acid set of a position-specific scoring matrix.

Find homologous protein-protein interactions across multiple species.

Search your query sequence against PROSITE pattern database for protein motifs.

Find information about protein-RNA complexes from the Protein Data Bank (PDB).

Identify protein families and domains for a given protein sequence.

A comprehensive database of pattern-recognition receptors and their ligands.

Search for short nucleotide or peptide sequences such as cis-elements in nucleotide sequences or small domains and motifs in protein sequences.

Database specialized in documenting human PPBD-containing proteins and PPBD-mediated interactions.

Predicts potential protease cleavage sites and sites cleaved by chemicals in a given protein sequence.

Predict combined transmembrane topology and signal peptides.

Search for eukaryotic phosphorylation sites.

Search for 3D structure and functional annotation of phosphorylation sites in proteins.

Search the database of in vivo phosphorylation sites of human and mouse proteins

Find information about polyglutamine (polyQ) repeats.

Find the presence of protein motifs and patterns in an amino acid sequence.

Predict signal peptide sequences and their cleavage positions in bacterial and eukaryotic amino acid sequences.

Predict protein functions based on known structures.

Predict the location of potential protein-protein binding sites for unbound proteins.

Identify short linear signatures in protein termini.

Analyze and identify newly obtained protein sequences.

Predict protein binding sites in a protein sequence based on geometrical analysis of protein tertiary substructures.

Search for evolutionarily conserved motif-like patterns in protein sequences.

Web-based server for analyzing and predicting RNA binding sites in proteins.

Search for similarities between proteins by simultaneous matching of multiple motifs.

Predict residues in protein sequences that determine the proteins' functional specificity.

Predict specificity-determining residues in protein families.

Find shared motifs in proteins with a common attribute.

Conduct in silico sumoylation sites prediction.

Search for motifs and patterns within protein sequences.

Search for motifs within proteins that are likely to be phosphorylated by specific protein kinases or bind to domains such as SH2 domains, 14-3-3 domains or PDZ domains.

Find information about populations carrying polymorphisms within protein binding pockets that make them susceptible to serious adverse drug reaction (SADR).

Search the presence of a motif in either amino acid sequence or nucleotide sequence.

Predict the presence of tyrosine sulfation sites in protein sequences

Look at protein structure from a ligand and binding site perspective.

Use a collection of bioinformatics tools at this portal site.

Find information about tandem repeats in proteins that carry fundamental biological functions and are related to a number of human diseases.

Find information about functional residues in alpha-helical and beta-barrel membrane proteins.

Database of domains and motifs with conservative location in transmembrane proteins.

Search for highly conserved and specific protein sequence motifs.

Predict functional sites in protein sequence alignments use different methodologies.

Find de novo protein motifs from chromatin immunoprecipitation data.

Scan query structures for functional sites in both proteins and nucleic acids.

Analyze quantitative structure-activity relationship of related protein families.

Search for ungapped alignments of highly conserved regions among a protein family or superfamily.

An expert system for predicting ligand-binding residues in protein structures.

Automatically identify spatially interacting motifs among distantly related proteins sharing similar folds and possessing common ancestral lineage.

]]> Neel https://bioinformaticsonline.com/bookmarks/view/41996/wgd%E2%80%94simple-command-line-tools-for-the-analysis-of-ancient-whole-genome-duplications Thu, 23 Jul 2020 05:49:45 -0500 https://bioinformaticsonline.com/bookmarks/view/41996/wgd%E2%80%94simple-command-line-tools-for-the-analysis-of-ancient-whole-genome-duplications <![CDATA[wgd—simple command line tools for the analysis of ancient whole-genome duplications]]> wgd is a easy to use command-line tool for KS distribution construction named wgd. The wgd suite provides commonly used KS and colinearity analysis workflows together with tools for modeling and visualization, rendering these analyses accessible to genomics researchers in a convenient manner.

https://academic.oup.com/bioinformatics/article/35/12/2153/5162749

Address of the bookmark: https://github.com/arzwa/wgd

]]> LEGE https://bioinformaticsonline.com/blog/view/43260/bioinformatics-tools-for-telomere-to-telomere-assembly Tue, 17 Aug 2021 13:17:09 -0500 https://bioinformaticsonline.com/blog/view/43260/bioinformatics-tools-for-telomere-to-telomere-assembly <![CDATA[Bioinformatics tools for telomere to telomere assembly !]]> ● Merfin – k-mer-based assembly and variant calling evaluation for improved consensus accuracy (Arang Rhie)
● PanGenie – algorithm that leverages a pangenome reference built from haplotype-resolved genome assemblies in conjunction with k-mer count information from raw, short-read sequencing data to genotype a wide spectrum of genetic variation (Tobias Marschall)
● SQANTI3 – an automated pipeline for the classification of long-read transcripts that can assess the quality of data and the preprocessing pipeline (Rocío Amorín de Hegedüs @rocioadh)
● tama (Transcriptome Annotation by Modular Algorithms) – software designed for processing Iso-Seq data and other long-read transcriptome data (Richard Kuo @GenomeRIK)
● pbaa (PacBio Amplicon Analysis) – separates complex mixtures of amplicon targets from genomic samples to cluster and generate high-quality consensus sequences from HiFi reads (Zev Kronenberg @zevkronenberg)
● bellerophon – analyzes MHC typing and other low-complexity gene amplicon data; performs allele calling while detecting polymorphic sites within the sequences and removing potential chimeric sequence variants (Yuanyuan Cheng @Yuanyuan929)
● svpack – tools for filtering, comparing, and annotating structural variant (SV) calls in VCF format (Aaron Wenger)
● JumboDB – tool for de Bruijn graph construction (Anton Bankevich @AntonBankevich)
● uLTRA – tool for splice alignment of long transcriptomic reads to a genome, guided by a database of exon annotations. (Kristoffer Sahlin @krsahlin)
● LeafGo – workflow to rapidly produce high-quality de novo plant genomes (Luca Ermini @ermini_luca)

Reference:

https://www.pacb.com/blog/young-investigators-share-stellar-science-career-advice-and-bioinformatics-tools-at-smrt-leiden-2021/

 

]]> BioStar https://bioinformaticsonline.com/blog/view/21257/bioinformatics-phds-cover-letter Mon, 16 Feb 2015 12:48:52 -0600 https://bioinformaticsonline.com/blog/view/21257/bioinformatics-phds-cover-letter <![CDATA[Bioinformatics PhDs - Cover Letter]]> Overview
The reception your cover letter will receive is more varied and unpredictable than the other elements of your application packet.  Some readers, especially at large research universities, will skip it entirely, and focus instead on more direct indicators of your academic achievements and potential: the CV, letters, and writing sample.  Most often, however, your cover letter works in tandem with your CV, and represents your best opportunity to communicate directly with your target audience prior to an interview.

The cover letter should not simply repeat the information contained in the CV; rather it should elaborate and frame the aspects of your academic training and background that you want search committee members to have uppermost in their minds as they consider your candidacy.  At a minimum, it should contain a clear statement of your research and teaching interests, and how your qualifications match the requirements of the job description. Many disciplines have their own conventions and protocols for application materials including the cover letter. Be sure to show a draft of your letter to your chair or the department's placement advisor.

There is no perfect or even preferred style of cover letter save that tone should be that of a confident professional.  Departments aren't interested in hiring graduate students (they already have enough of those).  They are looking for serious scholars/teachers who will make interesting, congenial, and productive colleagues. While it is probably not a good use of your time to tailor your letter for each opening, you will probably need at least two base versions that emphasize different elements (You would not want to use the same cover letter to apply to Oberlin as UC Irvine). You may want to customize the letters for the three to five jobs most attractive to you.

Your cover letter should not exceed a page and a half or two at the most.  No one will read a four-page letter, and your apparent inability to communicate your credentials in a concise manner is not to your advantage. Every line of your letter should serve a demonstrable purpose. Some people have gotten excellent jobs with a brief, boilerplate letter containing no more than a short intro, a paragraph on their research, and a couple of sentences on their teaching. Others use letters that discuss in very specific terms how they, their research, and their teaching would "fit" within the existing department and institutional setting. The choice of style is up to you, and should reflect what makes you feel most comfortable and most positive about your credentials.

There are, however, circumstances where a longer, more annotated version is more likely to be helpful. The issues you should take into account when making this decision are:

    The size of the department/institution
    The extent to which your research is mainstream and its significance readily apparent
    The extent to which your qualifications diverge from those mentioned in the job ad
    The extent to which the institution to which you are applying differs from UC Berkeley and the relative importance of teaching versus research
    Unusual circumstances or career paths

Size matters. The larger the department, the less detailed your letter needs to be.  If there are already six people working in your sub-field, they can assess and translate the significance of your work to others in the department.  If, however, you will be the only history of science person or the only physical anthropologist in the department, the search committee and others will likely need more help from you to understand the import of your work.  Remember, if they are searching for your specialty they are understaffed in your area, and even if there is more than one person in your sub-field she/he may be on leave or otherwise uninvolved in the search.

Similarly, smaller departments are more likely to be concerned than larger ones about the specific courses you are prepared to teach.  If there are certain core courses your position needs to cover, make it unambiguously clear that you are prepared to do so even if you haven't taught them already.

As a rule of thumb, the more your profile diverges from the specifics of the job description (or the norm, such that it exists), the more likely you will want to say more about why you are nonetheless a strong candidate.  By doing so you can turn a question mark into an advantage.  Highlight your strengths, and if you have time perform a little research on the department (see below) so you can explain how you would add to the department in ways that they might otherwise not have considered.  Job descriptions are not always etched in stone. If you're very interested in the job, call the department and ask to speak to someone on the search committee.  If that's too intimidating, call the departmental assistant or secretary. They can at least tell you if it’s a new position or replacement which in turn can indicate whether they really need someone who can teach The Politics of Southern Africa or if Middle Eastern Politics would do just as well.  They may well be willing to sacrifice a course in an area that the ad specified in order to get the extra teaching experience or innovative research that you have to offer, but only if you give them the opportunity and explain what they'd get in return.

 If you've taken an unusually long time to finish because you spent three years learning a new language in order to prepare for two years of original research or some other factor that enriches what you have to offer as a teacher or a scholar, let them know about it.  Learning a new language, for example, is an indicator of your commitment to serious scholarship.  Think of a way to put a positive valence on an aspect of your CV that you believe is likely to raise questions or doubts.

As hard as it may be for not-yet-employed-but-soon-to-be academics like yourselves to believe, there are some individuals and departments that are intimidated by you and your institutional pedigree.  There are many fine schools and departments that are convinced that every Berkeley graduate student is only interested in a position at Harvard, Princeton, or (hold your nose) Stanford.  If it is a small liberal arts college, they may also assume that you view teaching and advising undergraduates strictly as a necessary evil. Ergo, why interview someone who will either look down their nose at us, and/or desert us for greener pastures as soon as their third book has been published and Yale comes calling.  They often don't seem to realize that Berkeley graduate students are sincerely interested in a wide range of academic settings for a variety of reasons personal and professional, and, perhaps more to the point, Yale rarely "comes calling."  An even greater burden of proof exists if a quick scan of your CV reveals that your all of your experience in higher education has occurred in a large, elite, research-oriented setting. How can you overcome this potential obstacle especially if you're very interested in a school that you have reason to believe may be suspicious of Berkeley PhDs?

It's probably not advisable to write in a cover letter that they shouldn't be intimidated by lil' old you (i.e., "I'm really not very good; in fact, my advisor says I'm her worst graduate student in twenty years."), but there are ways to indicate a sincere interest in their department.  One means is to do a little research, and briefly discuss how you might fit into the department and the institutional community-at-large.  There are numerous articles written by faculty about the search process that state explicitly the positive impact it can have on their impression of a candidate to see evidence that that he/she has taken the time to learn something about them. Researching a department and its associated programs/research centers has never been easier.  Virtually all colleges and universities are online, and their web addresses can be easily located using Education/Universities link.

A second way to address fears of imminent flight is to provide evidence of ties to the area, the university, or lifestyle.   If you are from the Midwest, have family nearby, or even if you just spent an enjoyable summer there, add a line about it and your desire to return to your cover letter for Purdue or Wayne State.  If you did your undergraduate work at a similar type of institution, draw their attention to that link.  Don't assume that they will notice where you got your BA on your CV, and make the connection.

If you are applying to small, liberal arts colleges, don't just list the courses you've taught with a line of boilerplate about how important you take your teaching responsibilities to be. These days, a good, small college has a list price of about $30,000 a year, and close contact with people like you is one of their key selling points to parents and potential students. You are expected to be more than a lecturer, and your ability to convey your recognition of that fact and a sincere interest in working closely with students matters. Advising, participating in non-academic activities, watching your students grow and mature inside the classroom and out (and having an influence on that process) can be among the most rewarding aspects of the profession.  If you value these broader elements of being a professor, let them know. In particular, if your own experience has been limited to large universities, think about describing an episode where you had a positive impact on the development of a student and the satisfaction you derived from helping him or her. At many small colleges, and large universities (including Berkeley) as well, one of the most significant pedagogical trends is fostering greater student involvement in faculty research.  If you can articulate how this might occur for you and their students in a brief but thoughtful manner, it can alleviate some of the concerns occasioned by your Berkeley background.

Your letter typically will have four segments: the introduction, research, teaching, and the closing.

Introduction
Be sure to identify the position you are applying for by rank and sub-field in the first sentence or two.  It is not uncommon for departments to be engaged in more than one search in a given year, and large departments may have more than one in your sub-field.  If it is an open rank position and you are applying as an assistant professor, in most cases you and your record of accomplishments will not be directly compared with those of more experienced candidates applying for it at the associate or full professor level.  Rather, applicants are assessed based on where they are in their career path, and you have every chance of successfully competing against them.

If you have finished, mention it up front. If not, state when you expect to file - no later than June 2003.  Many readers will view your optimistic prediction with a skeptical eye, and anything you can say that makes the claim appear more credible (e.g., "I have written and my chair has reviewed four of the six chapters.") can help mitigate their concerns.  You can put such a statement in the introduction or wait until you discuss the dissertation itself.

If your discipline holds its annual meeting in the summer or early fall (i.e., before application deadlines) and you had an excellent conference interview, make reference to it in the cover letter including the names of the faculty with whom you spoke.  By the time people actually start to read files, months may have passed and even the strongest of impressions can fade.  But they can be rehabilitated and revived, especially if you can remind them of a specific strength, ability, or issue that seemed particularly salient during the interview.
Research
Unless you are applying to a school that cares only about your teaching (increasingly rare), a description of your research generally follows next.  The challenge here is not simply to describe your research, but to frame it in terms of your sub-field and discipline. The search committee, hasn't lived, eaten and breathed Post-Edwardian Hermeneutics for the past five years the way you have. And they haven't been there every step of the torturous process like your friends and significant others. So forgive them for not immediately recognizing your research at first blush for the path-breaking work that it is.

Departments want to know that in hiring you, they are adding someone who will make a future contribution to the discipline and enhance the reputation of the department. However, they are often ill-equipped to understand exactly how that will be true in your particular case. Many disciplines are sufficiently broad that leading or cutting-edge research in one sub-field is barely intelligible to those in others.  In addition, when you became a candidate for the short list, your file will be read by department members outside your specialty, and, oftentimes later on in the process, by individuals outside your discipline as well. You need to describe the forest in which your tree resides, and explain why it matters in terms of the broader trends and issues within your discipline. Obviously, if your work is focused on one of the classic conundrums of your field, much less in the way of providing perspective is necessary than if you are addressing an emergent issue or employing an unconventional approach.

If your research is particularly novel or cutting-edge, any markers of broader acceptance by other, more established scholars or scholarly organs in your field can ameliorate possible concerns about its relevance and potential importance.  In discussing your work, note the recognition it has received in the form of competitive grants, awards, publications in refereed journals, and/or presentations at major conferences.

It is also important to mention where you expect your research to go after the completion of your dissertation and the publications that will flow from it.  They, especially at research-oriented institutions, want to know at least in broad terms where you expect to go from here.  They want to see evidence of a scholarly agenda that extends beyond the dissertation.  You don't need to have pages written or titles blocked out, but you need to tell them in a paragraph, (two at the most) about what questions intrigue you, and how you expect to go about finding the answers to them.  These questions for future research may have been generated by the findings of your dissertation, unusual data uncovered during your fieldwork, or interesting side issues that you were forced to put off in order to keep your dissertation taut and focused.  Think twice about mentioning future projects that appear entirely unrelated to your current work. Departments will want you to be firmly established in one area before you go off into another.

The relative importance of teaching versus research is a continuous and not a discrete variable.  Even schools that emphasize teaching in their job listing will generally want evidence of scholarly engagement and publishing potential. It is a very competitive market for students out there, and virtually all institutions are under pressure to sell themselves via the quality of their faculty to the limited number of good students in the applicant pool.  A good marker for the relative importance of teaching versus research is the teaching load.  An institution with a 3-2 teaching load (a total of five courses taught per two-semester year) will expect more in the way of and value research more highly than one with a 3-3 load.

If you work in a capital-intensive area, mention your track record of gaining grants and other sources of external funding.
Teaching
In a similar fashion, the more the institution you are applying to diverges from Berkeley and the more your profile differs from the job description, the more expansive you should be in talking about your teaching and what you have to offer their students.  If you have won a teaching award at Berkeley, don't make them wait until they read your CV to discover that fact.

For letters sent to large universities where you will be expected to teach large, lecture courses and graduate seminars, little is needed except to convey that you have the necessary experience and/or background.  Since the dominant pedagogical style is the same as that found at Berkeley, it will be assumed that you will be able to do the same for them.

If you have TA'd some of the courses that (based on the job description) you would be expected to offer, let them know.  If you have not had that opportunity, but your fields and research fall within the domain of the job description, you may want to add a line about how well prepared you are to teach such courses based on your training and research.

If your profile does not correspond exactly to the description found in the job announcement, but you believe that you could nonetheless teach the required courses, explain the basis for your confidence. What strength would you bring to the department that would more than make up for your "otherness"?  Departments may decide that they don't "need" another conventional European historian if you can convince them that you can handle the core courses and offer something new that they don't currently cover as well. In this case especially, think about doing some research so you can speak in more specific terms about how you would fit into and enhance the department's offerings. Help them visualize how you would strengthen them as a department.

Small colleges will want to know that you are able to teach on a more intimate basis and are prepared to take the time to do it well. If you give them three generic lines about how "important" you consider your teaching responsibilities, don't expect much of a positive response.  Spend a couple of paragraphs describing your teaching experience and philosophy, and how you would take advantage of the opportunity to create you own courses.

If you've had the opportunity to design and teach your own course, tell them, briefly, how you went about it and the choices you made. Don't just say you're a good teacher, tell them why. Look over your teaching evaluations and pick out one or two consistent strengths to highlight. Drawing on your experience, talk about how you engage students and enhance their skills (especially writing) and intellectual development.

For good, small colleges, the quality of the teaching offered to students is their stock-in- trade.  Even the most research-oriented, small colleges take teaching undergraduates very seriously. You need to demonstrate that you take it seriously as well, and can talk about it using more than vague generalities and shop-worn cliches.
Closing/Enclosures
Wrap it up quickly.  If you are going to be traveling for any significant portion of the job search season be sure to let them know how they can contact you (email, cellphone and/or a Pacific Bell message center account are particularly good options).

Before closing, include a sentence where you list the materials you have enclosed (e.g., CV, dissertation abstract, transcripts, teaching statement/portfolio, writing samples, etc.) and are having sent separately (letters of recommendation).  You will often be assembling multiple packets at the same time (as in midnight October 14th, midnight November 14th, etc.) for jobs that ask for different combinations of enclosures.  In addition to telling them what they should have received, it serves as a handy list for you to check before sealing the envelope.

Should you send a writing sample, even if they haven't asked for one? Unless the norm in your discipline is to never send more than they ask for, if you've been smart and have already run off thirty copies at two cents per page (that's only sixty cents for a thirty page chapter) by all means send it.  That way it's in the file should someone become interested in you and want to read more.

]]> Martin Jones