Textco BioSoftware today announced the release of Gene Inspector® version 2.0. Gene Inspector (GI) offers users a unique combination of a comprehensive sequence analysis product integrated within a versatile electronic laboratory e-notebook. By combining DNA and protein analyses with a powerful notebook, Gene Inspector provides the ability to integrate research notes and analysis outputs.

The new Gene Inspector version 2.0 features updated analysis routines for faster processing, offers support for the latest operating systems for both Mac and Windows, and researchers will notice smoother font and analysis displays producing crisper notebook entries.

The unique design of Gene Inspector has won praise from researchers because it stands alone in allowing biologists to create “suites of analyses” and assign each with a name. This unique feature allows a researcher to setup the analyses for an entire lab – then any user in a group can choose the suite from a menu and perform analyses on new sequences to obtain directly comparable results. Gene Inspector’s analysis suites can provide a consistency never before available – making for more meaningful sequence analysis and consistent results from different users.

Interactive analyses allow scientists to pursue unique investigative paths by using one analysis result to launch a new analysis. In addition, the notebook has inventive features like the ability to “hot link” your sequences to analysis results to keep results updated should sequence change. “Bookmarks” within the notebook permit easy navigation through large projects, and you can use “appendices” to store information such as buffer recipes and assays. Gene Inspector allows biologists to follow their thought processes as they analyze sequences and take notes.

The feature set of Gene inspector®, complements the features offered in Gene Construction Kit® – the Premier Plasmid Mapping Software – also developed by Textco BioSoftware.

Gene Inspector 2.0 compatibility:
* Mac OS 10.5 and higher (including Lion and Snow Leopard)
* Windows 7, Windows Vista & Windows XP

To mark the release of GI 2.0, special Introductory Discount Pricing is being offered – both for upgrades for current users, and for new licenses for labs looking to add the efficiencies offered by Gene Inspector.

A free demo version of Gene Inspector 2.0 is available for download.

Contact for Sales/Marketing:

To mark the release of the New Gene Inspector® 2.0 …
TWO special GI “Introductory Discount” offers are available – take your pick!
An added option – get GCK at a 50% discount! (see offer below)

Gene Inspector® offers a powerful combination …
* DNA and protein sequence analysis software
* A versatile electronic laboratory e-notebook

Gene Inspector® 2.0 is New and Improved…
* Updated analysis routines in GI 2.0 are designed to run faster
* Smoother font and analysis displays produce crisper notebook entries
* Support for the latest operating system updates under both Mac and Windows

TRY the New GI 2.0 yourself -> Ask for a FREE FULL-FUNCTIONING Trial Version

How can GI help you add consistency to your research efforts?…
Create “suites of analyses” and assign each with a name. This unique feature allows a researcher to setup the analyses for an entire lab – then any user in a group can choose the suite from a menu and perform analyses on new sequences to obtain directly comparable results.

The feature sets of Gene Inspector® and
Gene Construction Kit® -The Premier Plasmid Mapping Software-
complement each other.

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SAVE on GI alone -or- with GCK - Your choice through June 30th…

1:  Save on GI -> Plus you have the OPTION to add GCK:
~ Save $200 on a Gene Inspector® a single-computer license* purchase;
~ With this GI purchase -> ADD Gene Construction Kit® at a 50% discount.

Mention code ‘GI200‘ to Save $200 usd on GI;
Mention code ‘GCK50‘ to add GCK for 1/2 price with your discounted GI purchase.

- OR -

2:  Buy One GI  – Get One GI FREE:
Purchase one Gene Inspector single-computer license at the regular price and receive the second Gene Inspector absolutely FREE

~ Get 2 Gene Inspector licenses for the price of 1 ~
Mention code ‘GI2for1‘

Offers Expire June 30, 2012 – Act Now!

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

FREE demo versions for Gene Inspector 2.0 (and Gene Construction Kit®, our award-winning plasmid mapping application) are available online for:
* Mac OS 10.5 and higher (Including Lion)
* Windows 7, Windows Vista & Windows XP

Academic, government, and commercial discount pricing is offered, and for installation on 10 or more computers ‘Network’ type licensing is available.

* Current promotions are for NON-EXPIRING licenses.
* Discount offers cannot be combined.
* Excludes prior purchases, annual licenses, and network renewals.

Contacts for Sales/Marketing:
Brant Hackett; 480-241-9121
Roberta Brucks Gross; 603-643-1471

About Textco BioSoftware
Textco BioSoftware (formerly Textco, Inc.), has been developing high quality productivity tools for molecular biologists for over 25 years. Our unwavering commitment to customer service, and our focus on quality has generated a loyal customer following. Since 1984, we have provided solutions to scientists who are breaking new ground in genetic engineering, basic biology research, drug development, and biotechnology – at academic, government, and corporate institutions in more than 50 countries worldwide.

West Lebanon, NH 02|15|2012

To review this latest Newsletter in your browser, please click here.

In the February 2012 newsletter, Textco BioSoftware featured a ‘Refer a Colleague’ promotion offering a FREE annual license with a referral, or with the purchase of a single-computer GCK 3.5 license.

The newsletter also announced the public availability of GCK version 3.5.3. This latest update to the program offers expanded support for searching by and for degenerate nucleotide characters, enhancements for large restriction enzyme site searches, an update to the GenBank parser for support for non-standard files, and read/write improvements for working with files in mixed platform environments.

The complete list of changes and enhancements included in this release can be reviewed by visiting the ‘Revision History‘ page.

All current GCK 3.0 and 3.5 license holders are encouraged to update to this latest release for improved performance. Visit the updates section of the website to download the FREE update.

If you have any suggestions for topics you would like to see covered, please email us with your thoughts!

Textco BioSoftware today released GCK version 3.5.3, available to current customers as a FREE download from the updates section of the website.

Users will find expanded support for searching by and for degenerate nucleotide characters, enhancements for large restriction enzyme site searches, an update to the GenBank parser for support for non-standard files, and read/write improvements for working with files in mixed platform environments.

The complete list of changes and enhancements included in this release can be reviewed by visiting the ‘Revision History‘ page.

All current GCK 3.0 and 3.5 license holders are encouraged to update to this latest release for improved performance. There is NO charge.

For customers with older GCK licenses (version 2.5 or previous), please contact us for special upgrade pricing.

GCK 3.5 is compatible with Mac OS 10.4 and higher (Including OS 10.7, “Lion”); and Windows 7, Windows Vista & Windows XP.

New customers can evaluate Gene Construction Kit 3.5 by downloading the free demo version.

Contact Textco BioSoftware for more information.

Contact for Sales/Marketing:

Brant Hackett; 480-241-9121

Roberta Brucks Gross; 603-643-1471

About Textco BioSoftware

Textco BioSoftware (formerly Textco, Inc.), has been developing high quality productivity tools for molecular biologists for over 25 years. Our unwavering commitment to customer service, and our focus on quality has generated a loyal customer following. Since 1984, we have provided solutions to scientists who are breaking new ground in genetic engineering, basic biology research, drug development, and biotechnology – at academic, government, and corporate institutions in more than 50 countries worldwide.

West Lebanon, NH  09|28|2011

Textco BioSoftware today released GCK version 3.5.2, available to current customers as a FREE download from the updates section of the website.

Modifications and fixes included in this release can be reviewed by visiting the ‘Revision History‘ page.

All current GCK 3.0 and 3.5 license holders are encouraged to update to this latest release for improved performance.

GCK 3.5 is compatible with Mac OS 10.4 and higher (Including OS 10.7, “Lion”); and Windows 7, Windows Vista & Windows XP.

New customers can evaluate Gene Construction Kit 3.5 by downloading the free demo version.

Contact Textco BioSoftware for more information.

Contact for Sales/Marketing:

Brant Hackett; 480-241-9121

Roberta Brucks Gross; 603-643-1471

About Textco BioSoftware

Textco BioSoftware (formerly Textco, Inc.), has been developing high quality productivity tools for molecular biologists for over 25 years. Our unwavering commitment to customer service, and our focus on quality has generated a loyal customer following. Since 1984, we have provided solutions to scientists who are breaking new ground in genetic engineering, basic biology research, drug development, and biotechnology – at academic, government, and corporate institutions in more than 50 countries worldwide.

Textco BioSoftware today announced the public beta testing for Gene Inspector version 2.0 for Mac. The Windows beta release will be available soon. GI 2.0 is compatible with Lion (Mac OS 10.7), as well as previous versions of Mac OS (10.4 or higher).

Users will notice faster analysis processing, smoother font and analysis display via anti-aliasing, and GI 2.0 is a Universal Binary release – taking advantage of the Intel processors found inside the latest Macs.

We are beginning an open beta-test for GI 2.0 and encourage you to participate by signing up here.

Contact for Sales/Marketing:

This is a great deal of data! How can we make sense of it all? Clearly, there is valuable information and insights into the biology, if only we could tease it out of this deluge of data. Most usefully, we should turn the raw data into knowledge and understanding. As humans, the challenge is too great without the use of computers. Designing computer algorithms to analyze these data sets is where the challenge lies. New algorithms have to continue to be developed to explore the data in a meaningful way.

One approach that is particularly intriguing for computational biology is to model computer algorithms on biological systems. There is a satisfying symmetry to this approach for the biologist. This blog entry will talk about neural networks, but future blogs will address evolutionary computation and ant colony optimization – other computational approaches based on biological models. These three approaches will give you a good feel for how the biology can help shape the computation.

Biological neural networks, as they exist in your brain, consist of interconnected neurons that can accomplish a task – such as recognizing a tree, your spouse, a problem or a solution. Multiple input neurons can connect to a common target, each having a different “strength” to their influence on the target neuron. Similarly, multiple target neurons, might connect to their common target neuron. These neuronal circuits help to generate the output, for example: “Oh, that’s a tree!” In reality, the connections are more complex, but this is the essence – neurons connecting to other neurons to form a network.

Artificial neural networks work on the same principle [7]. For example, let’s say we want to predict protein secondary structure from the primary amino acid sequence. Methods exist that do a decent job of this using more classic approaches [8, 9]. We might choose to have 9 input neurons (sensors, perceptrons) that recognize 9 adjacent amino acids in the sequence . These might feed into three target neurons (via 9 x 3 = 27 connections). The three target neurons might, in turn, feed into a single output neuron (three more connections) that can then predict the likely structure for this stretch of 9 amino acids (or the middle 3 or 5 amino acids). The output value of the output neuron can specify that the structure is alpha helix, beta sheet, or random coil (or some other structure). The 30 different connection weights are what actually determines the output and the analysis process of the input data. How are those weights determined?

The weights are determined by the neural network program that is initially trained on a set of proteins whose structure is known. At first, the weights are randomly assigned and the network is evaluated by how well it predicts the structures for a set of proteins with known structures. Next, the weights are adjusted and the network run again. If the accuracy improves, the next round of adjustments takes place in the same “direction” as the previous round. If the new network is worse than the previous one, the weights are reset and the process is rerun. There are many details involved in a lot of these steps. Decisions have to be made on how to measure the accuracy of the network, how to adjust the weights, can connections have zero weights, how many layers does the neural network have, how many “neurons” are there in each layer, can signals go backwards so that a layer can alter the value of a previous layer, etc. The performance of the program can be influenced dramatically by these choices. In any case, the connection weights of a network evolve (nice word!) towards a more accurate predictor. Each iteration of the network gets better and better until an optimum solution is reached.

Once the network is trained (the connection weights do not change significantly from one iteration to the next), it can be used to predict the structure of unknown proteins which can then be tested in the lab. Note that since the values are randomly chosen to begin with, each time a nueral network is trained on a set of data, it could produce a different result. Thus, it is usually advisable to train the network multiple times to ensure getting a well trained network.

It is possible to generate a number of different trained neural networks that perform equally well despite having dramatically different weights for the connections. Therein lies a weakness of the approach. Even if a neural network is perfect (100% accurate), it might not be possible to learn what is actually being modeled by just looking at the weights and connections. Basically, the neural network is a black box that takes some input and generates an output. Many feel that a black box that works is better than no method at all. If the neural network is successful, it says that the input data is sufficient to predicting the output (even if it is not understood how it is working). With that understanding, it should be possible to design a new algorithm with a precisely defined model that can do what the neural network was able to do. This would be classified as progress.

Neural nets have been developed to address a number of different biological systems. Actual protein secondary structure prediction (as in our simple example) has been approached by neural networks [10, 11] and the results are an improvement over more traditional methods [8, 9]. GRAIL is a neural network that has been developed to predict genes from genomic DNA sequences [12]. Other biological uses for artificial neural networks include modeling D1 and D2 dopamine receptors [13], designing bioactive proteins [14], predicting antigenic activity for hepatitis C protein NS3 [15], and inferring the rules of E. coli translational efficiency [16].

What artificial neural network solutions have in common is that they mimic a biological system to guide the computing. Principles that work in biological systems have been applied to computing systems with quite a bit of success. Survival of the fittest seems to work for computing as well as for biology. It is easy to imagine a positive feedback loop where using biological computation will help us understand the actual biology. This in turn will lead to more detailed and sophisticated biological models that can then be applied to redesigned and improved algorithms… It will be fun to see how this all evolves .

References

1. Markowitz, V.M., N.N. Ivanova, E. Szeto, K. Palaniappan, K. Chu, D. Dalevi, I.M. Chen, Y. Grechkin, I. Dubchak, I. Anderson, A. Lykidis, K. Mavromatis, P. Hugenholtz, and N.C. Kyrpides, IMG/M: a data management and analysis system for metagenomes. Nucleic Acids Research, 2008. 36(Database issue):D534-538 http://www.ncbi.nlm.nih.gov/pubmed/17932063.
2. Barrett, T., D.B. Troup, S.E. Wilhite, P. Ledoux, C. Evangelista, I.F. Kim, M. Tomashevsky, K.A. Marshall, K.H. Phillippy, P.M. Sherman, R.N. Muertter, M. Holko, O. Ayanbule, A. Yefanov, and A. Soboleva, NCBI GEO: archive for functional genomics data sets–10 years on. Nucleic Acids Research, 2011. 39(Database issue):D1005-1010 http://www.ncbi.nlm.nih.gov/pubmed/21097893.
3. Sayers, E.W., T. Barrett, D.A. Benson, E. Bolton, S.H. Bryant, K. Canese, V. Chetvernin, D.M. Church, M. DiCuccio, S. Federhen, M. Feolo, I.M. Fingerman, L.Y. Geer, W. Helmberg, Y. Kapustin, D. Landsman, D.J. Lipman, Z. Lu, T.L. Madden, T. Madej, D.R. Maglott, A. Marchler-Bauer, V. Miller, I. Mizrachi, J. Ostell, A. Panchenko, L. Phan, K.D. Pruitt, G.D. Schuler, E. Sequeira, S.T. Sherry, M. Shumway, K. Sirotkin, D. Slotta, A. Souvorov, G. Starchenko, T.A. Tatusova, L. Wagner, Y. Wang, W.J. Wilbur, E. Yaschenko, and J. Ye, Database resources of the National Center for Biotechnology Information. Nucleic Acids Research, 2011. 39(Database issue):D38-51 http://www.ncbi.nlm.nih.gov/pubmed/21097890.
4. Matys, V., E. Fricke, R. Geffers, E. Gossling, M. Haubrock, R. Hehl, K. Hornischer, D. Karas, A.E. Kel, O.V. Kel-Margoulis, D.U. Kloos, S. Land, B. Lewicki-Potapov, H. Michael, R. Munch, I. Reuter, S. Rotert, H. Saxel, M. Scheer, S. Thiele, and E. Wingender, TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Research, 2003. 31(1):374-378 http://www.ncbi.nlm.nih.gov/pubmed/12520026.
5. Huerta-Cepas, J., S. Capella-Gutierrez, L.P. Pryszcz, I. Denisov, D. Kormes, M. Marcet-Houben, and T. Gabaldon, PhylomeDB v3.0: an expanding repository of genome-wide collections of trees, alignments and phylogeny-based orthology and paralogy predictions. Nucleic Acids Research, 2011. 39(Database issue):D556-560 http://www.ncbi.nlm.nih.gov/pubmed/21075798.
6. Maddison, D.R., K.-S. Schulz, and W.P. Maddison, The Tree of Life Web Project. Zootaxa, 2007. 1668:19-40 http://www.mapress.com/zootaxa/2007f/zt01668p040.pdf.
7. Hopfield, J.J., Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the United States of America, 1982. 79(8):2554-2558 http://www.ncbi.nlm.nih.gov/pubmed/6953413.
8. Chou, P.Y. and G.D. Fasman, Prediction of the secondary structure of proteins from their amino acid sequence. Advances in enzymology and related areas of molecular biology, 1978. 47:45-148 http://www.ncbi.nlm.nih.gov/pubmed/364941.
9. Garnier, J., D.J. Osguthorpe, and B. Robson, Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. Journal of molecular biology, 1978. 120(1):97-120 http://www.ncbi.nlm.nih.gov/pubmed/642007.
10. Guermeur, Y., C. Geourjon, P. Gallinari, and G. Deleage, Improved performance in protein secondary structure prediction by inhomogeneous score combination. Bioinformatics, 1999. 15(5):413-421 http://www.ncbi.nlm.nih.gov/pubmed/10366661.
11. Cai, Y.D., X.J. Liu, and K.C. Chou, Prediction of protein secondary structure content by artificial neural network. Journal of computational chemistry, 2003. 24(6):727-731 http://www.ncbi.nlm.nih.gov/pubmed/12666164.
12. Uberbacher, E.C., D. Hyatt, and M. Shah, GrailEXP and Genome Analysis Pipeline for genome annotation. Current protocols in bioinformatics / editoral board, Andreas D. Baxevanis … [et al.], 2004. Chapter 4:Unit4 9 http://www.ncbi.nlm.nih.gov/pubmed/18428726.
13. Karolidis, D.A., S. Agatonovic-Kustrin, and D.W. Morton, Artificial neural network (ANN) based modelling for D1 like and D2 like dopamine receptor affinity and selectivity. Medicinal chemistry, 2010. 6(5):259-270 http://www.ncbi.nlm.nih.gov/pubmed/20977414.
14. Huang, R.B., Q.S. Du, Y.T. Wei, Z.W. Pang, H. Wei, and K.C. Chou, Physics and chemistry-driven artificial neural network for predicting bioactivity of peptides and proteins and their design. Journal of theoretical biology, 2009. 256(3):428-435 http://www.ncbi.nlm.nih.gov/pubmed/18835398.
15. Lara, J., R.M. Wohlhueter, Z. Dimitrova, and Y.E. Khudyakov, Artificial neural network for prediction of antigenic activity for a major conformational epitope in the hepatitis C virus NS3 protein. Bioinformatics, 2008. 24(17):1858-1864 http://www.ncbi.nlm.nih.gov/pubmed/18628290.
16. Mori, K., R. Saito, S. Kikuchi, and M. Tomita, Inferring rules of Escherichia coli translational efficiency using an artificial neural network. Bio Systems, 2007. 90(2):414-420 http://www.ncbi.nlm.nih.gov/pubmed/17150301.

Textco BioSoftware today released GCK version 3.5.1, available to current customers as a FREE download from the updates section of the website.

Modifications and fixes included in this release can be reviewed by visiting the ‘Revision History‘ page.

All current GCK 3.0 and 3.5 license holders are encouraged to update to this latest release for improved performance.

GCK 3.5 is compatible with Mac OS 10.4 and higher (Including OS 10.7, “Lion”); and Windows 7, Windows Vista & Windows XP.

New customers can evaluate Gene Construction Kit 3.5 by downloading the free demo version.

Contact Textco BioSoftware for more information.

Contact for Sales/Marketing:

Brant Hackett; 480-241-2191

Roberta Brucks Gross; 603-643-1471

About Textco BioSoftware

Textco BioSoftware (formerly Textco, Inc.), has been developing high quality productivity tools for molecular biologists for over 25 years. Our unwavering commitment to customer service, and our focus on quality has generated a loyal customer following. Since 1984, we have provided solutions to scientists who are breaking new ground in genetic engineering, basic biology research, drug development, and biotechnology – at academic, government, and corporate institutions in more than 50 countries worldwide.

West Lebanon, NH 05|24|2011

Textco BioSoftware today announced the release of an upgraded version of their award-winning DNA plasmid mapping software, Gene Construction Kit® (GCK 3.5).

GCK version 3.5 includes enhancements for both Windows and Mac. Users will notice increased speed when working with files accessed over the network, and GCK 3.5 offers a number of other improvements designed to increase efficiency.

In anticipation of the pending Apple operating system upgrade, this GCK release is compatible with Mac OS 10.7 “Lion”. The Mac version of GCK 3.5 takes full advantage of the processors found inside the Intel Macs and is completely updated to Universal Binary.

A FREE Upgrade is being offered to current GCK 3.0 license holders. Those customers can simply visit our support page to follow the complimentary update instructions.

To mark the release of GCK 3.5, Textco BioSoftware is offering special ‘Introductory Discount‘ pricing to both new customers -and- labs looking to expand the use of GCK to additional users.

Since it’s introduction more than 20 years ago, Gene Construction Kit® has been the molecular cloning software of choice for tens of thousands of researchers worldwide. GCK has proven the test of time, and its unique, graphical approach to cloning has not been matched. Lauded by leading researchers as the ‘Easy-to-learn AND Easy-to-Use’ cloning package – researchers rely on GCK to simplify the management of their cloning projects. This upgraded version of GCK includes enhancements that will save researchers both time and money. A complete list of the updates can be viewed through Gene Construction Kit’s version history.

GCK 3.5 is compatible with Mac OS 10.4 and higher (Including Snow Leopard and the soon to be released Lion OS); and Windows 7, Windows Vista & Windows XP.

A free demo version of Gene Construction Kit 3.5 is available for download.

Contact Textco BioSoftware for more information.

Contact for Sales/Marketing:

Brant Hackett; 480-241-9121

Roberta Brucks Gross; 603-643-1471

About Textco BioSoftware
Textco BioSoftware (formerly Textco, Inc.), has been developing high quality productivity tools for molecular biologists for over 25 years. Our unwavering commitment to customer service, and our focus on quality has generated a loyal customer following. Since 1984, we have provided solutions to scientists who are breaking new ground in genetic engineering, basic biology research, drug development, and biotechnology – at academic, government, and corporate institutions in more than 50 countries worldwide.

Textco BioSoftware today announced the public beta testing for GCK version 3.5 for both Mac and Windows. The Mac version has been completely updated to Universal Binary to take full advantage of the processors inside the new Intel Macs. Both the Windows and Mac version of GCK 3.5 will have new font panel menus, increased speed for working with networked files, right-mouse button click support for edit operations, updated GenBank importer options and other enhancements to increase your efficiency.

We are beginning an open beta-test for GCK 3.5 and encourage you to participate by signing up here.

Also in our February 2011 Newsletter, we highlight the ‘Gel Window’ of GCK and how it can help confirm results and save you time and money.

To review this latest Newsletter in your browser, please click here.

If you have any suggestions for topics you would like to see covered, please email us with your thoughts!