bio+tech

Understanding Translational Effects of Variants With SnpEff

Mo — Tue, 30 Apr 2013 00:22:05 +0000

Once we have assembled the genomes of our subject(s)[1][2], generated a list of variants, annotated these variants with relevant databases (e.g. dbSNP)[3], we may now be interested in investigating the structural and translational effects of genomic variants on proteins.

Interacting with protein structures in VMD

If the interest in structural variations is well-intentioned then it behooves us to use SnpEff, which both adheres to VCF 4.1 standards and GATK best practices. As with many of the downstream processes we must make an initial investment by choosing a reference build, which for human samples, at the moment, consists of GRCh37 and HG19. Install the necessary reference library and run SnpEff:

$java -Xmx[allocate memory] -jar snpEff download [reference library]

$java -Xmx[allocate memory] -jar snpEff eff -v -onlyCoding true 
-i vcf -o vcf [reference library] [input].vcf > [output]

It should be noted that the choice of reference build is not necessarily arbitrary. The same reference genome that was used for assembly, should have also been used for variant detection, and this rule remains constant for use in uncovering translational effects in SnpEff. Otherwise, the user will be met with a “No Tribble Type” error. Correctly executed, the INFO field of our VCF file will contain the new additional annotations:

SNPEFF_AMINO_ACID_CHANGE=E281*
SNPEFF_CODON_CHANGE=Gag/Tag
SNPEFF_EFFECT=STOP_GAINED
SNPEFF_EXON_ID=NM_032269.ex.6
SNPEFF_FUNCTIONAL_CLASS=NONSENSE
SNPEFF_GENE_BIOTYPE=mRNA
SNPEFF_GENE_NAME=CCDC135
SNPEFF_IMPACT=HIGH
SNPEFF_TRANSCRIPT_ID=NM_152727

Above we see the fields filled in with a sampling from within the gene CCDC135. Below we can see how this data appears within an intact VCF 4.1 file, which can be parsed to pull out the desired details.

If we choose to create a file containing translation effects which also adheres to GATK best practices, there are a few additional steps, however recent studies have shown that while GATK pipelines are designed to improve results, they don’t always tend to do so[4].

$java -Xmx[allocate memory] -jar GenomeAnalysisTK.jar 
-T VariantAnnotator

-R [reference].fasta -A SnpEff --variant [raw].vcf 
--snpEffFile [snpeffoutput].vcf

-L [raw].vcf -o [gatk_snpeff_output].vcf

The process outlined in this post will bring users closer to understanding how genomic variants cause changes in protein structures and possibly lead to functional insights. Other tools such as SIFT and PolyPhen are also promising in aiding the study of translational changes, investigators are encouraged to compare tools and share opinions. Good luck!

Pair-end Sequence Assembly With BWA, Samtools, & Picard

Mo — Thu, 06 Dec 2012 23:19:38 +0000

The following post is a continuation of a series comparing sequence assembly strategies, the previous post in the series can be found @PetriDishTalk.

Burrows-Wheeler transform has, since the turn of the millennium, become a staple of trusted sequence assembly, while used in many data compression methods it has seen significant utility in NGS algorithms.

Great explanation of Burrows-Wheeler transform on Wikipedia

The Burrows-Wheeler Alignment Tool (BWA) will often show up in publications, and is included as part of the Broad Institute’s “Best Practices.” Thus making it a prudent choice for any alignment strategy. With BWA we must construct an index of our reference genome. Some careful thought should be given as to which reference one uses, as it will determine compatibility with downstream algorithms for variant detection, annotation, and other processes.

$bwa index -a bwtsw reference.fasta

The Genome Reference Consortium is a good place to get references, but they may not be the most compatible with the downstream tools one decides to use, making other choices i.e. UCSC hg19, more applicable.

Building the BWA reference index can take up to several hours

Note: on Mac OS X 10.8.2 the newest BWA releases, bwa-0.6.2, will build an index without error however this index will contain inconsistancies and missing files which will hinder analysis downstream. Consequentially, it is recommended on up-to-date OS X using bwa-0.5.9. Once the index is built, sequence alignment indices of our raw fastq files need to be built, fastq file structure is discussed in the previous post in this series.

$bwa aln reference.fasta short_pair_1.fastq > short_pair_1.sai 
$bwa aln reference.fasta short_pair_2.fastq > short_pair_2.sai

We can now combine our reference index, the two alignment indices, and our two pair-end raw fastq files to create a single sequence alignment map.

$bwa sampe reference.fasta short_pair_1.sai short_pair_2.sai 
short_pair_1.fastq short_pair_1.fastq > short_pair.sam

This step is the most resource/time intensive and the final output is an unsorted sam file, which must be converted to binary and then sorted using samtools as discussed in the previous post. Importantly, there are some quirks of a BWA alignment that must be dealt with using Picard. Firstly, BWA uses “*” as a flag for unaligned reads when “0″ is preferred/required. Second, mate-pair info must also be adjusted. Third, we may have to add read headers to our file; all of this can be done with Picard.

$java -Xmx[memory]g -jar ValidateSamFile.jar I=[.bam or .sam]
#displays MAPQ flag error, mate-pair error, and header error
...
$java -Xmx[memory]g -jar FixMateInformation.jar I=[.bam] O=[.bam] VALIDATION_STRINGENCY=LENIENT
#requires significant scratch disk space, recommend utilizing $ -Djava.io.tmpdir = /scratch_directory
TMP_DIR=/scratch_directory
...
$java -Xmx[memory]g -jar ValidateSamFile.jar I=[fixed.bam] IGNORE=INVALID_MAPPING_QUALITY
#validate file accepting MAPQ flag "*"
...
$java -Xmx[memory]g -jar AddOrReplaceReadGroups.jar I=[.bam] O=[.bam]
LB=anything PL=illumina PU=anything SM=anything
...

We will have to re-sort the Picard output one last time, and all of this will be worth it, as we will be left with a high quality assembly that meets stringent quality standards and interfaces well with variant discovery & annotation tools present in well-tested packages, i.e. the GATK.

This post is the part of a set providing initial documentation of a systematic comparison of various pipelines with a wide range of algorithms and strategies.

Computing Infrastructure for Large-Scale Genomics

Mo — Mon, 01 Oct 2012 19:00:45 +0000

The large amounts of data stored within a human being is complex and once the flood gates are opened it’s difficult not to be overwhelmed. Three approaches have been taken traditionally to deal with this data, local implementation, outsourcing to informatics “factories”, and distributed/cloud-based solutions. Knome “The Human Genome Interpretation Company” released a local solution recently which makes a rough claim to complete all the informatics work for a human genome from assembly to variant calling within one day. That is, 30 genomes per month, per knoSYS.

knoSYS™100

One could imagine wheeling this into a academic laboratory, hospital, or industrial research park. But at $125,000 each and the rate of change in computing hardware, I would be surprised if purchasing such systems are anything more than a gross portrayl of conspicuous consumption on the side of the buyer. A proven, and value-driven method for access to large-scale bioinformatics has been to work with “sequencing factories” i.e. JCVI, Broad Inst. The J. Craig Venter Institute for example, maintains a 1000 node Sun Grid Engine (SGE) cluster utilizing Hadoop / MapRecuce and employs approximately 57 bioinformaticians and software developers.

JCVI

Practically speaking however, both of the aforementioned models pale in comparison to consumer cloud-based solutions such as AWS. Where the user can have as many equally or more powerful nodes with the same or better software at scalable costs.

Extra Large Instance

15 GB memory
8 EC2 Compute Units (4 virtual cores with 2 EC2 Compute Units each)
1,690 GB instance storage
64-bit platform
I/O Performance: High
EBS-Optimized Available: 1000 Mbps
API name: m1.xlarge

High-Memory Quadruple Extra Large Instance

68.4 GB of memory
26 EC2 Compute Units (8 virtual cores with 3.25 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
EBS-Optimized Available: 1000 Mbps
API name: m2.4xlarge

Individual nodes on Amazon EC2 are nearly hardware-equivalents to the knoSYS. And they can be scaled instantly. Now there are even more robust cloud solutions from efforts such as Nebula from NASA Ames, which are tailored to research specific applications, especially genomics. Nevertheless, one issue remains which is the transfer of such large datasets from the sequencing machines to the analysis clusters, and it has now arguably become the bottleneck.

Next-Generation Sequencing Statistics

This has been an area of much debate, how do you get the data from it’s point of origin, whether an academic lab, a hospital, or even large “sequencing factory” to where it needs to be processed, accessed and perhaps even stored. It turns out nature can efficiently store and move around petabytes of data with ease, at the drop of a hair. However, humanity’s workarounds to this problem at the moment involves filling shipping crates with hard disks and transporting it with conventional cargo. Thus, there still remains some need for local computing.

The moSYS™600 – It runs on bourbon.

This need to close the gap between large data generation and efficient analysis must be overcome for adequate adoption of genomics in conventional medicine. While some players in the field such as Knome will try to sell a pre-packaged solutions in a box, others such as Nebula are betting on the “private cloud”. While for academic labs and small businesses and hackers, I’d still recommend a mix of your own local solution backed by a powerful cloud pipeline. Of course, the real solution would be to have a modern internet infrastructure, but that delves quickly in the realm of civic policy, unless you live in Kansas City.

The Day-long Think Tank

Mo — Mon, 14 May 2012 02:07:25 +0000

With the summer blockbuster film season starting we are getting to see teams of superheroes come together and save the earth by putting aside their differences and working together for brief periods of time. The traditional view of the life sciences is one where slow, steady progress is made over the lifespan of ones career. This is how innovation and discoveries are made, or so we were taught [1].

Manhattan Project Scientists in 1940 Meeting at UC Berkeley

However, recent advances in other fields, have shown us that this is not the only plausible model. Hackathons, are usually a day or two day long gatherings where software engineers come together to either work on a guided problem or on their own projects. These gatherings have had very notable successes of late, spinning off many companies, new tools, and significant discoveries [2]. It has been said, more frequently of late, that more than just in technology the information, consumer, and social web have advanced the way technical organizations operate. In many ways not only are the products of these companies evolving at a faster rate than the traditional life sciences, the organizational structures and operational protocols themselves leave much to be desired in the traditional sciences.

The Assemblathon is a set of periodic collaborative efforts that all help improve methods of genome assembly.

Hackathons are just the tip of this iceberg, one which we discuss to a relative depth in this piece. As mentioned above, the idea of bringing together the best minds in a particular field for a day or two in order to solve guided or open-ended problems is a stark contrast to the gradual nature of scientific research as usual. Of course the most obvious counter-point to applying this form of problem solving structure to life science research is the nature of working with hardware, and materials, “wet science” as it has been dubbed. Here it is prudent to distinguish and acknowledge data generation and analysis as the bottlenecks in life science research today. Whether we are speaking of genomics, pharmacology, or the large hadron collider, researchers are being drowned by a tsunami of data [3]. Additionally, the previously adequate research dissemination structure of scientific publishing within established journals is being exposed as a highly monopolized and inaccessible medium [4]. This is but a glimpse into the problems we are faced with. However, it is exactly the information related nature of these obstacles that make them ideal for the concentrated and unpredictable undertakings of a hackathon.

SHDH is a party for hackers and thinkers, combining serious and not-so-serious productivity

There are two approaches in guiding solutions via this method, hands on and hands off, just like most other things. The hands on approach involves setting goals, determining metrics of success, and stratifying the participant population. At the polar end is a laissez-faire approach, where a time, place, and beer are provided, the rest is left up to probability [5]. The key in both approaches is gathering the right people for the right problem. Though in the latter approach it is of upmost importance, in addition to assuring those involved have a thorough understanding of the nuances of the particular field of focus. In some ways it can be argued that the Manhattan Project was a progenitor of what may yet be the science hackathon, although much more dramatic in scope and time, but perhaps not in what is at stake.

Closing The Gap Between Computational & Pharmaceutical Innovation

Mo — Tue, 03 Apr 2012 18:39:48 +0000

When confronted with the mortality of life, it becomes painfully clear that medicine has not been able to keep up with information and computational innovations. At the heart of the problem stands the drug development process, where an average of 5 to 10 years of research and billions of dollars worth of investment often fails to produce a product.

Figure 1 | Probability of success to market from key milestones. Data: cohort of 14 companies.

In the past few years, molecules in development have seen a frightening rate of attrition. The most capital and resource intensive period comes during the clinical trials, which can be broken-down into the following stages: Phase I trials evaluate if a new drug is safe, Phase II and Phase III trials assess a drug’s efficacy, monitor side effects, and compare the drug to similar compounds already on market. Recent studies by the Centre for Medicines Research, places Phase II success rates at 18%, lower than at any other time during drug development [1]. Spending on average of $300 million to $1 Billion up until this point of research is par for the course [2].

Figure 2 | Computer-assisted screenings and traditional discovery strategy distributions of new molecular entities (NME). Followers are in the same class as previously approved drugs.

By contrast, computational drug design strategies have made tremendous advances in the new millennia with new tools to identify targets and virtual screening assays. These include structure-based tools to lead identification and optimization utilizing X-ray crystallography. As well as, high-throughput target-based screenings of key protein families like G protein-coupled receptors. Promising indicators of computational drug designs are encouraging new companies to court Big Pharma, who to-date have relied on academia or internal projects for computation. For a company like GeneDrop, even a fraction of the development budget would be adequate to deliver favorable results.

Drug development’s addressable market-size for global corporations such as Novartis or Roche, which have between 20-100 molecules in the pipeline at a given time, is estimated at $1.11 Trillion in 2011; down from $1.24 Trillion in 2001 [2]. There are approximately ten large pharmaceutical companies and many small ones with one or two late-stage molecules in development.

Fig 3 | Early-stage computational drug design flow

To-date, most computation in the space has been limited to early-stage research on the discovery of molecules prior to the clinical trial phases. However, the fall in market cap has sent drug companies scrambling as patents on existing blockbuster drugs near expiration, and those in development see increasingly high failure rates. This begs the question: why are computational resources being spent in the early-stage, when most failures occur in the late-stage, during Phase II

Fig 4 | Pharmacogenomics attempts to correlate how individuals will respond to drugs based genomic variability.

As always, cost has been a primary factor. Late-stage computation has meant analysis of bio-metric data, which has been limited to blood-work and questionnaires of trial subjects. The pie in the sky of course, has always been genomics, the price of which was deemed too high. Even up to a couple of years ago, it would cost over $10,000 to sequence an individual. With Phase II and III trials consisting of hundreds to thousands of patients, the method was rarely used. As of the last few months this is no longer the case, with the cost hovering around $5,000 and quickly approaching $1000 per patient.

So, we are faced with an enticing opportunity for information technology to rescue a high-capital, old-world industry. Threading this needle however is no easy task; entrenched industries with high quarterly revenues are notoriously conservative when adopting innovation, especially from the outside. Adding to this is the high barrier of the technical languages of the hard-sciences and the networking culture of global corporations. Luckily both are boundaries which have been broken before in other industries and we can be optimistic; if anyone can break it, it is the passionate and talented.

A cross-post by Mo from petridishtalk.com

Citations:

[1] Trial watch: Phase II failures: 2008–2010 by J. Arrowsmith – Nature Reviews Drug Discovery 10, 328-329 (May 2011) | doi:10.1038/nrd3439

[2] - Fig 1- A decade of change by J. Arrowsmith – Nature Reviews Drug Discovery 11, 17-18 (January 2012) | doi:10.1038/nrd3630

[3] – Fig 2- How were new medicines discovered? by David C. Swinney & Jason Anthony - Nature Reviews Drug Discovery 10, 507-519 (July 2011) | doi:10.1038/nrd3480

[4] – Fig 4 - Genomics in drug discovery and development by Dimitri Semizarov, Eric Blomme (2008) ISBN 0470096047, 9780470096048

DNA & Iterated Function Systems

Mo — Mon, 27 Feb 2012 19:00:17 +0000

H. Sapiens

Genomic code that makes us is made up of four letters, ATGC. Billions of these letters together creates a lifeform. Iterated function systems (IFS) are anything that can be made by repeating the same simple rules over and over. The easiest example being tree branches, add a simple structure repeatedly ad-infinitum and before you know it we have complex and beautiful systems; the popular example being the Sierpinski Triangle or “triforce” for the Zelda fans. As the cost of DNA sequencing becomes cheaper day by day we are confronted with a tsunami of data and it has become exceedingly difficult to derive meaningful answers from all the information contained within us.

Triforce Power

Finding any advantage in ways to organize and view the data helps us discover minute differences between individuals or say a normal cell versus a cancer cell. This is where Chaos Game Representation (CGR) becomes helpful, CGR is just a form of IFS that is helpful in mapping seemingly random information, that we suspect or know to have some sort of underlying structure.

In our case this would be the human genome. Although when looking at the letters coming from our DNA it seems like billions of random babbles, it is of course organized in a manner to give the blueprint for our bodies. So let’s roll the dice- do we get any sort of meaningful structure when applying CGR to DNA? If you are so inclined, something fun to try is the following:

genome = Import["c:\data\sequence.fasta", "Sequence"];
genome = StringReplace[ToString[genome], {"{" -> "", "}" -> ""}];
chars = StringCases[genome, "G" | "C" | "T" | "A"];
f[x_, "A"] := x/2;
f[x_, "T"] := x/2 + {1/2, 0};
f[x_, "G"] := x/2 + {1/2, 1/2};
f[x_, "C"] := x/2 + {0, 1/2};
pts = FoldList[f, {0.5, 0.5}, chars];
Graphics[{PointSize[Tiny], Point[pts]}]

g1346a094 on Chromosome 7

For example, reading the sequence in order, apply T1 whenever C is encountered, apply T2 whenever A is encountered, apply T3 whenever T is encountered, and apply T4 whenever G is encountered. Really though any transformations to C, A, T, and G can be used and multiple methods can be compared. Self-similarity is immediately noticeable in these maps, which isn’t all that surprising since fractals are abundant in nature and DNA after all, is a natural syntax. Being aware that these patterns exist within our data, opens us up to some new questions to evaluate if IFS, CGR and fractals in general are helpful tools in the interpretation of genomic data.

Signal transducer 5B (STAT5B), on chromosome 17

Since the mapping is 1-1 and we see patterns emerge, we are hinted that there may be biological relevance; especially because different genes yield different patterns. But what exactly are the correlations between the patterns and the biological functions? It would also be very interesting to see mappings of introns/exons colored differently or color amino acids and various codons. One thing is for sure, genomes aren’t just endless columns and rows of letters, they are pictures. It is much easier to compare pictures and discover variations, which can ultimately allow us to find meaningful interpretations from this invaluable data.

A cross-post by Mo from petridishtalk.com

Citations:

Jeffrey, H. J., “Chaos game visualization of sequences,” Computers & Graphics 16 (1992), 25-33.

Ashlock, D. Golden, J.B., III. Iterated function system fractals for the detection and display of DNA reading frame (2000) ISBN: 0-7803-6375-2

VV Nair, K Vijayan, DP Gopinath ANN based Genome Classifier using Frequency Chaos Game Representation (2010)

Mo — Wed, 22 Feb 2012 02:25:56 +0000

Biotech for Hackers

Many software developers have extensive experience and interest in dealing with large data sets, finding correlations and creating meaningful solutions. However, much of our generation has had little exposure to these problems. Often resulting in the bandwagon effect. Fig 1 shows something very similar to social interaction maps one comes across at places like Facebook.

Fig 1: Interaction map of genes implicated in Alzheimer’s. Genes were grouped by those that have similar functions (squares) and those with different functions (circles). Modules with a red border have high confidence interactions. While the weight of the connecting green lines corresponds to the number of interactions between two sets.

The map above is of individual gene relationships where an algorithm began with 12 seed genes that previous experiments have shown to play a role in Alzheimer’s disease. These seeds were compared with 185 new candidate genes from regions deemed susceptible to carrying Alzheimer’s genes. From here, both experimental and computational data was combined to generate Fig 1, which the authors dubbed AD-PIN (Alzheimer’s Disease Protein Interaction Network).

A low hurdle to entry along with the ability to iterate rapidly is key to taking on problems & creating solutions. What do these solutions look like in genomics and why can hackers lead the way? Progress has often been linked to literacy, from books to programming, being able to read and write in life-code just might be the next stage.

A cross-post by Mo from petridishtalk.com

Original published study: Interactome mapping suggests new mechanistic details underlying Alzheimer’s disease by Soler-Lopez et al.

Try It, You’ll Like It

Mo — Tue, 21 Feb 2012 02:31:47 +0000

Normal Conference VS Developer Conference. SHDH Illustrated by Derek Yu

Attending gatherings for software developers in silicon valley, their hackathons leave much to be desired at bio events like Sagecon, the least of which being the beer.

Hopefully this will be a fun tool for folks not well acquainted with genomics/programming to sandbox and explore in.

The National Center for Biotechnology Information (NCBI) provides a command line based standalone Basic Local Alignment Search Tool (BLAST) package known as BLAST+ to analyze and play with genomic sequence data. Although, the legacy web based BLAST can perform a range of functions, BLAST+ as a command line tool is much better to understand and analyze large amounts of nucleotide data. It may be best to get an idea of what sort of data we’re dealing with by getting into the government’s database:

mokas$ ftp ftp.ncbi.nlm.nih.gov
Connected to ftp.wip.ncbi.nlm.nih.gov.
220-
 Warning Notice!

 This is a U.S. Government computer system, which may be accessed and used
 only for authorized Government business by authorized personnel.
 Unauthorized access or use of this computer system may subject violators to
 criminal, civil, and/or administrative action.

 All information on this computer system may be intercepted, recorded, read,
 copied... There is no right of privacy in this system.

Don’t worry about the scary message, this is all public data… well until the funding stops. Take a look in the blast/db directory for many pre-formatted databases NCBI has provided, i.e. genomic & protein reference sequences, patent nucleotide sequence databases from USPTO & EU/Japan Patent Agencies. Get yourself the latest BLAST+ from blast/executables/LATEST , I used ncbi-blast-2.2.25+-universal-macosx.tar.gz .

Installation:

mokas$ tar zxvpf ncbi-blast-2.2.25+-universal-macosx.tar.gz 
mokas$ PATH=/Users/mokas/Desktop/ncbi-blast-2.2.25+/bin
mokas$ export PATH
mokas$ echo $PATH
/Users/mokas/Desktop/ncbi-blast-2.2.25+/bin
mokas$ mkdir ./blast-2.2.25+/db
mokas$ blastn -help
USAGE
  blastn [-h] [-help] [-import_search_strategy filename]
...

Databases should be loaded directly into /db directory created above with the mkdir command. The last thing that needs to be done is to make a “.ncbirc” text file in the main directory containing the following:

[BLAST]
BLASTDB=/Users/mokas/Desktop/ncbi-blast-2.2.25+/db

This will guide the program to where data is being kept. At the end of the day we should hope to get something like this:

mokas$ blastn -query Homo_sapiens.NCBI36.apr.rna.fa -db refseq_rna
BLASTN 2.2.25+
...
Query=  ENST00000361359 ncrna:Mt_rRNA chromosome:NCBI36:MT:650:1603:1
gene:ENSG00000198714
Length=954
                                                                      Score     E
Sequences producing significant alignments:                          (Bits)  Value

ref|XR_109154.1|  PREDICTED: Homo sapiens hypothetical LOC1005054...   464    5e-128

>ref|XR_109154.1| PREDICTED: Homo sapiens hypothetical LOC100505479 (LOC100505479),
partial miscRNA
Length=266

 Score =  464 bits (251),  Expect = 5e-128
 Identities = 255/257 (99%), Gaps = 0/257 (0%)
 Strand=Plus/Minus

Query  334  CACCTGAGTTGTAAAAAACTCCAGTTGACACAAAATAGACTACGAAAGTGGCTTTAACAT  393
            |||||||||||||||||||||||||||| |||||||| ||||||||||||||||||||||
Sbjct  257  CACCTGAGTTGTAAAAAACTCCAGTTGATACAAAATAAACTACGAAAGTGGCTTTAACAT  198

BLAST+ in action.

Much thanks are in order to Dr. Tao Tao of NCBI

A cross-post by Mo from petridishtalk.com

Citations: Standalone BLAST Setup for Unix – BLAST® Help – NCBI Bookshelf

October 2011 bio+tech

Brad — Wed, 19 Oct 2011 22:19:50 +0000

Tuesday, October 18, 2010
Time: 7:00pm
Event Page: Eventbrite
Location: Giordano Bros. 3108 16th St, SF, CA (note, this is a brand new Giordano Bros – NOT the one on Columbus Ave in North Beach!)
Contact: windmiller[at]gmail[dot]com
Please RSVP!

The Scoop: On September 13th we had a fantastic kick-off for Bio+Tech at Giordano Bros in the Mission in SF! What a great turnout, and from eveyone I interacted with, I’d have to say that I was very impressed with the diversity of ideas, skill sets and people. Once again in October we’ll be teaming with our friends from Hackers and Founders to bring even more great people in to the mix! We hope you can make it – come, grab a beer and meet some fantastic new folks or even future collaborators!

As a note, many of you asked if there could be a medical and/or healhtcare slant to the meet up – of course! It’s all one big ecosystem – from consumer health, to new biopharma, to delivery models, and genomics – there’s tons of room for all.

We’ll be hosting this at the new Giordano Bros in the Mission – they have a shop in North Beach already, but this place is brand new! Nothing like having an entrepreneur event actually hosted in a start-up!

Please RSVP here at Eventbrite so we know how many people to expect! Thanks!