This page lists books and articles that give in-depth information
about topics we will cover in the lectures and lab. Specific procedures will be on the Course Protocols page.
Laboratory Techniques
Molecular Biology Methods
- "Molecular Cloning: A Laboratory Manual (Third Edition)”
by Joseph Sambrook and David Russell (2001)
Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY.
ISBN 978-087969577-4
- "Molecular Biology Techniques: An Intensive Laboratory Course"
by Walt Ream and Katherine Field.
Academic Press,1999, San Diego CA.
-
–
"Biotechnology: Proteins to PCR"
by David Burden and Donald Whitney
Birkhauser, 1995, Ann Arbor, MI.
–
-
"PCR: The polymerase chain reaction"
by Kary Mullis, Francois Ferre and Richard Gibbs.
Birkhauser, 1994, Boston, MA
-
"High Throughput Next Generation Sequencing: Methods and Applications"
in the series Methods in Molecular Biology from Springer Protocols
Humana Press 2011
edited by Young Min Kwon and Steven C Ricke
|
Introductory papers on some procedures we use
Spectrophotometry Platform manuals
Nanodrop platforms This overview has a marketing focus
but does give some of the limitations and best practices
Electrophoresis and Gels
Agarose Gels This provides a visual series of images on pouring andloading an agarose gel.
PCR papers
PCR Primer Design A tutorial walking you through primer design using a commonly available package.
|
Articles on DNA purification from plants
|
Articles on PCR primer design, reaction considerations and qPCR analysis tools.
- A tutorial from a class PCR tutorial
- A 2001 paper by Lampke et al describing what efficient primer design algorithms require. Primer Algorithms
- A 2003 paper from Steve Henikoff for a software program that helps design degenerate primers for related gene families (such as this project requires). CODEHOP
- Dr. Schlueter used the program HYDEN to design degenerate primers. Its' Web site is http://acgt.cs.tau.ac.il/hyden/. Several relevant papers are linked from that site, both theoretical and applied.
- I presented some discussion of what Primer3 parameters mean. VonAhnson et al recently provided a discussion about [Mg++] selection that may be useful as we optimize our multiplx amplicon reactions. Salt Corrections
- Analysis of Melting profiles helps improve the resolution of qPCR results, and is especially important for SYBR Green data. THe propgram uMELT is a rich web application from the Wittwer group, described here. How uMelt works
|
Articles on library construction methods for RNA-Seq experiments, and the kinds of bias they introduce.
- Random hexamers are often used to prime first-strand cDNA symthesis for library construction, but a non-random approach can help deplete some of the rRNA species, as this note describes Link
- The use of random hexamers lead to representation bias in the sequenced data - sources of this bias are explored in this article Link
- You can get more information from an RNA-Seq experiment if you maintain strand information. A comparison of the strategies is provided in this article. Link
- While most libraries use an intermediate approach to provide more starting material, it is possible to put adaptors on the RNA, attach the hybrid to the flowcell and perform sequencing directly, as described here. Link
|
Papers on more general concepts.
- Scientific Method
An editorial on the lack scientific method in many current articles.
An essay on why most published research findings are false. (There have been rebuttals and re-rebuttals).
- Responsible Conduct of Research
Evidence has come to light that infectious disease researchers knowling violated ethical standards in the 1940s. The Presidential Commission for the Study of Bioethical Issues (PCSBI)
has used this evidence to suggest additional measures for the protection of human subjects, including an insurance program to reimburse those found to have suffered the consequencese
of studies that violate the code. There are several popular articles on the subject
A Washington Post article by Rob Stein, "Compensation system urged for Research Victims" is linked here WP story
A NY Times article by Donald McNeil discusses the studies themselves here.
The Web site for the PCSBI is here; particularly relevant to bioinformaticians is the instantiation of a database of scientific trials, described in John Donelly's blog here.
- Ethics: genomics research combines some aspects of clinical research and genetic research. A nice summary of the issues is in this article in the Genome Web by Chris Rizk, "New places, Same Ethics": Web link
This is the BMC Medical Ethics paper by De Vries that is cited in that article: Link
The major concerns with genomics experimets include selecting the subjects and then preserving their confidentiality. Since microarray and NGS datasets contain more than enough data to identify individuals, this has lead to various anonymization strategies. Because even pooling with relatively large
numbers proved ineffective at 'hiding' an individual, there is also debate as to whether the best practice is to keep the data confidential. The following are some papers that describe several aspects of the debate.
- This is the pooling study from Homer et al. using SNP data showing that even large pools would be inadequate for anonymizing the presence of an individual, leading to a decision by the NIH to remove some raw datasets from their servers: Link
- Wjst gives several interesting examples of how SNP datasets can be used to violate confidentiality, and where the threats originate : Link
- McRae et al. examine the process of selecting subjects who participate in randomized trials - that selection actually implies some other information. In addition it may help you refine your understanding about the types of studies that need IRB approval, if you are not practiced in that area: Link
- As bioinformaticians we tend to believe in 'Open Data, Open Access'. Particularly when tax-payer funds supported the research we feel that maxiamal benefit accrues when the data is widely available, so meta-studies and new data mining approaches can makes use of the information. However, there is abalancing need to protect the privacy of study participants. Knoppers et al. outline some principles that a Genomic Research Code of Conduct for Data Sharing should encompass: Link
Links to offices and Web sites at UNCC:
|
Books and Articles on the Study Organisms
- A really classic paper from Kristofferson on the genetics of seed coat color (1924). Link
- A similarly classic paper on inheritance studies in soybean from Owen. Link
- A modern approach to the same problelm from McClean and Bassett in 2002. Link
- A description by Hosfield of the seed coat color genes in peas. Link
- A paper by Rick Dixon on the role of one enzyme in the flavonoid biosynthesis pathway. Link
- A introduction to the Plant Cell paper by Eckhardt on using siRNA silencing for CHS pathway genes in soybean. Link
- The Plant Cell paper by Tuteja et al (2009) on the Chalcone synthase pathway in soybean. Link
- A structural biology paper by Ferrer et al on the the chalcone synthase enzyme. Link
|
Articles on How to Model Genomics Experiments
- Using NGS to develop SNP markers from pooled samples - resolution and natural population considerations by Futschik and Schlotterer (2010). Link
- A short explanation of several pooling strategies from Patterson and Gabriel (2009). Link
- Experimental design specifically for RNA_Seq experiments from Auer, Brenner and Doerge (2010). Link
|
Review articles on Second-generation sequencing, some with short commentaries
- Picking a NGS platform for population targeted sequencing studies by Harismendy et al. (2009). Link
- A guide to the different types of next-generation sequencers and chemistries from Glenn (2011). Link
|
Random thoughts on Science
Common biases (from an article by Jeff Akst in The Scientist)
- Being risk averse (low innovation); good experiments explore new territory. If you always succeed you are probably not taking risks and you are not extending scientific knowledge.
- Adherence to a doomed model. Doing science requires a balance of persistence and willingness to modify a strategy or entire approach. If you give up too easily
you will lose new insights because of unresolved technical problems, but if a basic approach is flawed and you stick to it you will lose time. Sticking to a
an idea because you 'know it is right', in the face of contradictory evidence, is also called confirmation bias.
- If you are taking on important and difficult problems, you will run into a lot of failures. I usually say science is 95% failure and 5% success, and that 5% had
better be so rewarding that it compensates for the disappointments. Most scientists don't argue that ratio with me. Attributional bias arises when you have all your successes
in the first couple of years and ascribe that to being a great scientist, or have a long string of failures and ascribe that result to being a terrible scientist. In both cases
luck was the property at work. There is a lot of luck in doing science, and individuals should take neither credit nor blame for it - instead carry on (similar to investing in the stock market rather than
speculating in the stock market).If your experiments are well-designed you will learn as much from the failures as from 'successes'.
- Uncertainty about measurements and effects, sampling and populations (start with RA Fisher and just keep reading). Especially with new technologies that open vast areas of research, there have often been few experiments
about sources of error, noise and bias, from chemistry and biology through signal processing and software limitations. It is very easy to either over- or under-estimate the importance, relevance, significance and robustness of findings.
Publications no longer encourage discussions that suggest alternative mechanisms that might produce your observations, which I think is a shame.
|
Home
|
Course Schedule
|
Course Syllabus
|
Course Protocols
|
Course Readings
|
|
Faculty Home Dr. Jennifer W Weller
|
Top of Page