List of Papers

LIST OF PAPERS

MICROARRAYS:

REQUIRED READING

Dr. Sabine Mai

1-Genomic microarrays in the spotlight. (Download pdf file #1)

K. Mantripragada; P. Buckley; T. Diaz de Stahl and J. Dumanski.

TRENDS in Genetics, Vol. 20 No. 2 February 2004, .

2-News feature: Vital statistics.

Claire Tilstone.

Nature, Vol.424, 7 August 2003, .

Further references to consider from this paper:

#4, 5, 6, 7, 8 and 9.

3-Perspectives: Analysing differential gene expression in cancer.

P. Liang & A. Pardee.

Nature Reviews/ Cancer, Vol.3, 869-876, November 2003.

Further references to consider from this paper:

# 12, 14, 25, 26, 34, 35, 38 and 41.

Dr. Eric Leblond

4-References: list of over 1,800 publications available on website.

Dr. Jeremy Squire

5-A role for common fragile site induction in amplification of human oncogenes. (Download pdf file #2)

6-A tiling resolution DNA microarray with complete coverage of the human genome. (Download pdf file #3)

Dr. Timothy Stenzel

7-Genomic microarray in human genetic disease and cancer. (Download pdf file #4)

8-Determination of amplicon boundaries at 20q13.2 in tissue samples in human gastric adenocarcinnomas by high-resolution microarray comparative genomic hybridization. (Download pdf file #5)

Dr. Valia Lestou

9-Profiling breast cancer by array CGH. (Download pdf file #6)

10-Methods for high throughput validation of amplified fragments pools of BAC DNA for constructing high resolution CGH arrays. (Download pdf file #7)

Further reading:

16-Fauth C. & Speicher M. R. Classifying by colors: FISH-based genome analysis.

Citogenetics and Cell Genetics, 93 (1-2), 1-10 (2001).

Dr. Yuval Garini

17-Spectral Karyotyping. Yuval Garini et al. Bioimaging. 4, 65-72 (1996).

(Download pdf file #11)

18- Novel Imaging methods for biomedical applications.Yuval Garini

Imaging methods were greatly developed in the last ten years. Two of these methods will be discussed; multiple-probes detection and three dimensional structural analysis methods.

One of the requirements of modern biomedical applications is the simultaneous detection of many probes. By combining methods to detect multiple colors with high resolution imaging, several methods were developed that allow to detect a large number of probes. These methods include multiple color FISH (M-FISH), Spectral Karyotyping (SKY), Combined Binary Spectral Labeling (COBRA-FISH) and others. These methods are based on fluorescence in situ hybridization (FISH), combinatorial labeling and spectral imaging. They became a standard tool in molecular cytogenetics for both research and diagnostics.

Another important aspect is the observation of multiple genetic markers in situ by optical microscopy and their relevance to the study of three dimensional (3D) chromosomal organizations in the nucleus. Novel approach to study the distribution of all telomeres inside the nucleus of cells along the cell cycle will be described. It is based on 3D telomere FISH followed by quantitative analysis that determines the telomeres distribution in the nucleus along the cell cycle.

Listed below are papers and reviews that can be most useful for understanding the subjects described in the seminar.

Paper / Subject
M. R. Speicher, S. Gwyn Ballard, D. C. Ward, Nature Genetics12, 368-375 (1996). / First M-FISH paper – Observing all 24 human chromosomes.
E. Schröck et al., Science273, 494-497 (1996). / First SKY paper – Observing all 24 human chromosomes.

DECONVOLUTION

19-Dr. Peter Jansson: Exercise suggested by Dr. Jansson

To:CIHR Workshop Attendees

From:P. A. Jansson

Re:Preparation for Deconvolution Lecture

I am honored by the invitation to address your group. To best take advantage of the material presented, it would be helpful to work an exercise. I will connect the exercise to microscopy during the talk.

You can work the exercise in columns on a sheet of paper, on a spreadsheet like EXCEL, or with a simple computer program.

You are probably familiar with the concept of a weighted average. We are going to compute a simple moving weighted average of a list of sample values. Then, temporarily discarding the original sample values, from the resulting list of weighted average numbers, we will try to recover them.

Below we see columns containing indices, sample values, weights, and a single weighted average of the first 9 values, recorded in the fourth column and opposite the largest weight. The weighted average is obtained by multiplying each of the nine sample values opposite each of the weights by their respective weights, then adding those products together. Because the first nine values are zero, the sum is zero. For this particular set of weights, the sum is the same as the average because the weights are normalized, i.e., adding them together gives unity.

1234

SampleSampleWeighted

IndicesValuesWeightsAverage

10 x0.04=0 \

20 x0.08=0 |

30 x0.12=0 |(sum)

40 x0.16=0 |

50 x0.20=0 > =0

60 x0.16=0 |

70 x0.12=0 |

80 x0.08=0 |

90 x0.04=0 /

100

112 |

120 |

133 | (slide down)

140 V

150

160

170

180

190

200

210

220

230

240

250

260

Now, slide the nine weights down so that they are opposite sample values 2 through 10, and repeat the process. You will note that after the second sliding, the bottom weight has encountered the sample having a value of 2, so it then contributes to the weighted average.

When the bottom weight encounters sample 26, you are finished, and should have 18 numbers in the weighted-average column.

At this point it will be instructive to plot the original sample values and weighted averages on a “y” axis vs. the sample indices on the “x” axis.

Now, here is the part where you have to think. Suppose the original sample values are unknown or inaccessible to us. Let us try to recover those values, based on our knowledge of the weights and weighted averages alone, starting from the top of the weighted-average column. We are permitted to assume that the first eight values are zeroes. Because the weighted average of the first nine values is also zero, the ninth value must be zero too. Now it is up to you to consider the second weighted-average to see if you can infer the next original sample value on the list, having determined the previous one, and so forth. A little algebra will permit you to develop a formula for each new unknown sample value in terms of the previous values and the newly introduced weighted-average number. By this means recover and plot all the original sample values. Do not use "canned" deconvolution program(s) to perform this exercise. Carefully follow the directions given.

Were the sample values recovered correctly? (Hopefully “yes,” if you carried enough precision in your calculations.)

Now, add a small error, 0.02, to the weighted-average number opposite sample index 11, then repeat the recovery process, plotting the result. What did you observe?

Examples like this are contained in the introductory chapter of my text Deconvolution of Images and Spectra, Academic Press, 1997. This volume goes on to treat the mathematics and practical applications of deconvolution. For those who are sufficiently interested, this volume should be accessible to anyone with a background in elementary calculus. It and/or its first edition, Deconvolution, with Applications in Spectroscopy, Academic Press, 1984, can be found in nearly all major university libraries.

See you soon!